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About the Umbono Project
The Umbono Project is a project by incoming seniors from Mount Madonna School as part of the Values in World Thought Program. The students are currently on the learning journey to South Africa from June 14 until July 2, 2019.
Umbono means 'perspective'.
In South Africa we will be visiting a number of amazing programs, schools, and organizations that help support communities and people.
UMBONO SCHEDULE:
Friday June 14th:
Departure from California
Saturday June 15th:
Sunday June 16th:
Arrival in Cape Town
Monday June 17th:
Visit Robben Island
Interview with Thulani Mabaso
Visit Table Mountain
Tuesday June 18th:
Wednesday June 19th:
LEAP School – Langa Township
Thursday June 20th:
Interview with Mamphela Ramphele
Craft Market
Friday June 21st:
Interview with Pregs Govender
Saturday June 22nd:
Travel to Johannesburg
Sunday June 23rd:
Botshabelo Children's AIDS Village
Meeting with Chene Swart
Visit Tembisa Performing Arts
Tembisa Performing Arts – Rehearsal
Tembisa Performing Arts – Rehearsal and Performance for local Community
Visit Soweto
Craft Markets
Interview with Professor Piet Meiring in Pretoria
Travel to Pilanesberg National Park
Monday July 1st:
Depart South Africa
Tuesday July 2nd:
Arrival in California
Happiness Creates Happiness
Ksenia Medvedeva
I don't think I will ever enjoy singing as much as I did today. So far, our time at Tembisa has been full of talent, music, and warm-hearted kids who are thrilled to engage with us. As soon as my feet hit the dusty pavement, on our first day, I was swept away into a crowd of embraces and greetings. The kids at the Moses Molelekwa Performing Arts Center were buzzing with curiosity and the willingness to make connections with us.
Out of the sea of friends I made, one stands out. I met this kid named Thabang, and let me tell you he could make absolutely anyone and everyone laugh. A tall and rather lanky kid, he was hard to miss in a crowd. Honestly, my classmates and I were in conversation with him every chance we got. I will miss him and his overuse of, "guys!" very much.
It was during our last performance that I felt the most connected with our newfound companions. As our voices blended together as one, into a harmonious smoothie of sound and rhythm, I scanned the half circle that we had formed and felt a bolt of inspiration and empowerment. There was a silent energy that continued to build, all the way up to the very last song of our performance. Thulani, the choir director, informed us that it was a song that he himself composed. It was a tribute to the thousands of youth that marched through the streets of Soweto in June of 1976, as a means of protesting an apartheid law which enforced the use of the Afrikaans language in schools across South Africa. Towards the end of the song, we began to chant fiercely and march out into the crowd, pulling them into our performance. The power of our voices, and the energy that was vibrating all around us, was enough to make the room quake. In that moment, I think I understood why music is so important to me. It conveys emotions that words simply cannot generate. It brings people together. It helps build connections and paints beautiful memories that can be awakened with the simplest melody.
I talked to Thulani after the show, and he told me how he always closes his eyes when he listens to people perform. He explained to me that it showed him each layer to the full mural that was the song. I responded by saying that I understood him, and that his explanation was something that I had understood my whole life but never been able to explain. Along with the souvenirs I've bought and memories that I've collected, I hope to bring Thulani's spirit and passion home with me.
Tessa Ortiz
We spent our last day in Tembisa performing with our new friends. We also watched them perform scenes, songs, and dances. I had a chance to meet up with my new friends Zanele, Ellen, Zinhle, Junior, Princess, and many others. I really enjoyed it when they tried to teach me how to speak Zulu; it was incredible. It was amazing how inviting everyone was to a group of students they didn't know. I met so many people that came up to me with open arms and open hearts. We took pictures together to save the memories.
For the final performance, we walked in one by one. As we started singing, the crowd seemed moved by our expressions and power as a group. We owe Thulani a big thanks for being an amazing teacher. He rearranged several of our songs and taught us new songs in only three days. Our final performance moved our hearts and our bodies by the intensity and meaning of the songs. Most of our songs were chants or calls used in the fight against apartheid. When we got to our finale song, it felt like the whole room just came alive. People began dancing and smiling. We were full of joy, smiling and tearing up, knowing it was our final song together.
Our goodbyes were hard. I think it is amazing that in only three days we built such strong friendships that will last a lifetime. I hope to see them all again someday. I still can't believe how inviting the kids we met were. In the United States, we are used to being reserved, or observing each other, until we feel safe around new people. Here they want to learn about your culture and teach you about their culture. It's something you don't see all the time and we take it for granted. They seem proud of their history. Sometimes I feel ashamed of our past.
One of my new friends that I made at Tembisa said, "I will miss you." This warmed my heart. I am proud that we had made so many connections across the world and learned that in many ways they are just like us.
Jahnakai Willis
During my time at Tembisa, I learned that I can love a person within ten minutes of meeting them. This turned out to be the case for all the Tembisa students that we sang with. The third day we spent at Tembisa was the day of the performance; the day that would end with excruciating and bittersweet goodbyes. I think I've been saying the phrase, "This is the most fun I've ever had in my life," quite a lot on this trip, but our performance was more than fun, was more than exciting, to a point that I can't even explain.
The day started out with our huge bus pulling up to the now familiar group of dusty buildings and benches, greeted by our new friends in the courtyard. One boy, Sabelo said good morning by blowing everyone away with his insane beatboxing. As we slowly filed in around him, some people were just staring at him in awe, some were bouncing their heads to the beat, and some were strangely dancing. They did not even care what everyone else thought because we all felt so close and felt so much love that there was no judgment. While I have a special place in my heart for the Tembisa kids, I formed a friendship with one girl in particular. Her name meant happiness. Whenever I saw her, or made eye contact across the singing circle with her, she would break into a HUGE smile, making me smile, and we would both start dancing and laughing. I admit this caused Thulani to yell, "No dancing!" once or twice but I must say, Happiness created happiness.
After one of the best shows I've ever performed in, all the singers gathered around a man who was giving a speech and during that entire time, me and Happiness just hugged each other. Even though it was just a hug, I knew I had made a friend for life. She was the person who made me realize that I can love a person less than ten minutes after meeting them.
While I should probably be talking about how the performance went, instead I'm going to talk about the magic maker that created the show, Thulani. Thulani is literally one of the most amazing artists I've ever met. For some reason, Thulani is the kind of person who has a ridiculous amount of technique for putting things together and making everything that he creates sound stunning. I remember the first day that we walked into his studio and we were greeted by him and all the kids. Thulani asked us to sing for him so he could hear what we had prepared. I thought we did a pretty good job up until Thulani made us listen to the kids sing the song he had prepared, and my mouth fell open. To be honest, I was flushed with a little bit of embarrassment because I could tell he had worked his magic on those students but it also made me 1000 times more excited to learn as much as I could from him. Thulani isn't the kind of person who gets respect by yelling at kids until they're scared of him; he gets it from showing them what they can do if they work with him. He makes everything sound so good that even if you absolutely hate singing, you want to get up, clap your hands, sway your hips, and sing at the top of your lungs. With Thulani's gift, the day of the performance I wasn't scared or embarrassed whatsoever because I knew that our work with this short, excited, incredible man had prepared us to present our best.


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Slingshot SEO Answers the Question: Where are Journalists Going?
**An old British phrase based on the fact that fish & chips used to be wrapped in newspapers: today's news, tomorrow's (fish &) chip wrapper. Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.
Seo Blog Submitter 1.3 Full Version Info Blog Submitter allows you to search blogs based upon popular blog platforms (WordPress,Drupal,etc.) according to.
SEO 3.0: Man and Machine | gShift Labs Inc.


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Queen for a Day
Elissa Blattman
Pretend it is the 1950s. You are a recently single mother who needs a high school diploma so you can get a job to provide for your five children. Or, say, you need a wheelchair for your son who had an accident and became paralyzed. Or you need home repairs to erase the memory of when your husband, who was dying of brain cancer, decided to take his own life before the disease could. Would you go on national television, compete against other women's hardships, and hope the audience would select yours via applause-o-meter as the saddest so you could win a bunch of prizes? Thousands of other women did.
"Would you like to be Queen for a Day?" That question was asked to American women every weekday for 19 years on the long running radio and television show, Queen for a Day. The show has been called a precursor to today's reality television and "the worst program in TV history." Despite contemporary critics bashing the show and modern critics writing it off as unimportant to popular culture history, it dominated the ratings when it was on the air.
Queen for a Day debuted on radio on April 29, 1945 and on television on January 1, 1955. During its time on television, the show averaged 13 million viewers per episode and was number one in the ratings. A one minute commercial spot on Queen for a Day cost advertisers a whopping $4,000, and NBC cashed in $9 million each year from the show. The show gave away $23 million in prizes. It aired in the middle of the day, which meant its target audience was middle class housewives who were home to tune in. It went off the air in 1964. Its contestants were always women, except during occasional special episodes where a "King for a Day" or "Newspaper Boy King for a Day" was chosen.
For most of its run, Queen for a Day put four women up against each other to compete for prizes by sharing their hardships with the audience. During the voting round, the audience would applaud for who had the story most deserving of reward. Whoever received the loudest applause would be crowned "Queen for a Day." The queen would receive her "wish" prize (i.e. the wheelchair for said paralyzed son) plus a bunch of other prizes (i.e. a stove, sewing machine, and/or silverware). The losers left with consolation prizes (i.e. nylons). The woman who framed their wish in a way that suggested she would use it to help others usually won. By giving away material items to fill whatever void they had in their life, the message the show ultimately "seemed to suggest [was] that there was no problem, no loss that could not be fixed by the right product." It was packaged in a feel-good message that the middle class, post-WWII American society loved.
While Queen for a Day was certainly problematic in its exploitation of contestants, it stands out as an early venue though which underrepresented women actually had a voice and also as a slight criticism of the traditional gender roles of the day. Women went on Queen for a Day to discuss problems to which they had no other solution. Though its audience was primarily middle class housewives, the majority of the women who appeared on the show were working class or lower middle class. Such women contrasted the perfect middle class stereotype of suburban 1950s life and were rarely given a platform elsewhere. Many of their wishes centered around the fact that their husbands (if they had one) failed to provide financially for whatever it is they needed, or that they themselves could not provide for their families because of the lack of opportunities and education for women. Some women went on to express regret for not being able to pursue a career once they married and had kids (3:17-5:05 in the video below). Others turned to the show for help and an escape from abusive relationships. Women of color and immigrants went on Queen for a Day too and, in doing so, showed the nation that the same difficulties affected all women, regardless of race or nationality. In a time where minority women were often represented as parodies of themselves in the media, Queen for a Day allowed those women to be real and accurate representations of who they were. It allowed women to realize, at least on some level, that perhaps their misfortune was not solely their own doing, but was partially a symptom of the oppressive systems in place in society.
Though their appearances on the show did not really fix any problem they had, the Queens were launched into a temporary pseudo-middle class status by their winnings. At a time when that was both the pinnacle and standard for women, Queen for a Day allowed women "legitimate access to the female version of the American dream" that they were not given elsewhere
Ganas, Monica. "'Queen for a Day': Remembering Hollywood's Big First Makeover Show." The Wall Street Journal, June 12, 2010.
Poet, activist, and musician Joy Harjo became the first Native American United States Poet Laureate in history.
Appearing in over sixty movies throughout her career, Anna May Wong was the first Chinese American film star in Hollywood.
Janet Mock
Janet Mock became the first transgender woman of color to write and direct an episode of television.
Joyce Chen
Joyce Chen introduced Chinese food to the American public.

redpajama_set_name: RedPajamaCommonCrawl


A Feral or φλ-class World is an classification given to the most primitive human worlds of the Imperium.
The populations of feral worlds have long ago regressed to primal savagery, often over very long periods of isolation. The technological level of these worlds remains pre-black powder or even Stone Age. Often the population consists of hunter-gatherer societies using primitive tools and weapons, and in almost all cases human culture is based around the interactions of various tribes, gangs or creeds.
The harsh conditions which feral world populations have adapted to makes them ideal recruitment sources for the Imperial Guard and Adeptus Astartes. Occasionally the inhabitants of feral worlds have been pressed into the Imperial Guard when their world lay inside a war zone, and the chosen warriors been given rudimentary training in the operation of laser or stub-weaponry. It is more common, however, for feral world natives to be selected for the various Adeptus Astartes recruitment programs. Feral world Guardsmen are usually tribal warriors tithed to the Imperial Guard by the elders of their particular clan. They are sometimes trained en masse in vast drill camps or ship holds, whilst others are merely shown a lasgun and trusted to get on with it. Also, there are those instructed by members of their tribe, using rote-learned rites and religious rituals.
Like all Imperial worlds, feral worlds are ruled over by a Planetary Governor, although the nature of feral worlds makes this position somewhat different from that of Governorship of planets of other classes. The Governor of a feral world almost always lives apart from the natives, often living in a single city inhabited by outsiders or taking residence in orbit, only interfering to keep psyker and mutant "head counts" down. Religious heresy is also a regular concern on feral worlds, especially amongst warrior-cults; constant vigilance and regular belief-modification enacted by agents of the Ecclesiarchy are a necessity. Governors and permanent staff on such worlds are themselves kept under close scrutiny, in an effort to avoid the phenomenon of "going native", especially in situations where the inhabitants' belief systems have been manipulated into casting the Governor as a "god".
The culture shock associated with interaction with outsiders is an issue on feral worlds. Removing a feral worlder from their planet and exposing them to such things as warp travel can be disconcerting and even result in madness. Feral worlders within the wider Imperium often retain their superstitious and tribal idiosyncrasies, which may prove to be social hindrances, such as an obsession with bones of dead comrades or the mixing and regular application of noxious-smelling war paint. Other habits, such as manic distrust and aversion to 'witchcraft' may be seen as useful and sensible in the Imperium.
The Tithe Grades assigned to feral worlds are low, and are given as Solutio Tertius. The population can range from 100,000 to 5,000,000.
Typhon Primaris Ultima Korianis Subsector Aurelia Typhon System 300,000 Imperium Now — Dead World, also former Jungle World and recruiting planets for the Blood Ravens Space Marine Chapter.
This page was last edited on 9 February 2019, at 19:40.


The heart (Love) is the fulcrum for accessing the quantum field and, through it a co-creative power to shape our future. It is through the quantum connectivity of the heart that we bend time & space…returning home to the Source of creation within us.
When our brain aligns with the heart, a magical gateway is opened to quantum dimensions. Our heart is the core of the holograph that embraces the Whole. It is the entryway to a once hidden world of infinite fractal connection. The heart has power to align the immanent with the transcendent, the individual with the universal, the spectrum of developmental and imperfect levels in time-space with all-absolving grace of the infinitely spacious Unconditional Love of the Absolute. Our heart is the portal to graduated levels of perception in space-time leading to perfection of the all-forgiving, all-redeeming, all-embracing awareness of pure Love.
By bathing in fluidity of the quantum-field, we envision new portals of an enlightened future. It is through connection with subtle-field energies that the greater cosmic intelligence speaks to us, moves us; nudging us to new possibilities, releasing us from the stagnant silos of old mental constructs. Its fast-flowing, fluid frequencies visit as pure Light (not false-light), vibrations, shaking up (if necessary, as in kundalini experiences), dissolving old calcified thoughts and energies. Emergent evolutionary insights and impulses arise from sacred levels.
The mind will try to capture those subtleties, so be aware of this delicate balance. Mind, when receptive may catalyze fresh impulses into crystallized expression, but, in the process of formulating them, also tends towards calcification of them in a linguistic prison. We must rest in the heart with its subtle quantum connection for creative insight and guidance, living each vibrant moment attuned to heart-essence ready to receive its divine inspiration.
If God is Love, and everything is – why is there so much suffering? Many of us today, as throughout history, have wrestled with this question. Yet through wisdom of the bigger picture, a 360-View of evolutionary non-duality, some clues may be gleaned.
Unity cannot be experienced except through the voyage of duality. Eternity cannot be experienced except through the experience of time. Things cannot be experienced except in contrast to their opposite, so all that is "bad" in the world (the horrors) exist to drive us, awaken us to the goodness, the Love.
Originally there was only Love, but it splits itSelf into love and hate in order to make possible a return journey. When the hate – an illusion of separation – has reached such a peak that life on Earth has become a hell, it's natural to cry for help from on-high, for the return of the good great Love. Then the Love (good) must be brought back to bear on the "bad", the suffering – not to reject or judge it, but rather embrace, interpenetrate and dissolve it.
On the temporal level this happens slowly, by bringing Love to each thing, each situation or person in front of us, effecting an eventual material and social transformation – Heaven on Earth. On the trans-temporal level, the reconciliation and redemption is instantaneous and complete, accessible every moment through a shift in perception – by embracing darkness within Love, the two are no longer opposed to each other, and the darkness naturally merges into Love, the Light. Shadow is absorbed back into the Light, recognized as never having been separate. In the split-second when our personal grievances drop, this redemption is recognized within soul.
At the one level we have temporal transformation; at the other, trans-temporal transfiguration. Neither discounts the other; in fact, they feed and empower each other. Both are essential aspects of redemption. In a sense, we're learning Love the long way through evolution – learning what Love is.. by learning what it is not.
The steps in evolving from an old social paradigm to the next include: discernment about what's not working in the old system, and why; disengagement from outdated structures and identities; and positive re-engagement in an emerging New Earth sovereign worldview with fresh directions and actions flowing from it. In this flow, our energies and attention go to the positive, not getting stuck in an oppositional stance. Non-opposition is a part of non-duality, which both rests in its own fullness and is an open playing field for new possibilities unencumbered by either the force of the old or any loss of energy incurred in fighting against it.
Rather than an angry, confrontational opposition to "this or that" issue, or a "this or that" manifestation of the present delusional system, we need to get to the root of all issues – belief in separation. This is the root delusion now ruling our world. Our aim, and the need of the hour, is to move from divisive, oppositional, deluded duality-consciousness to unity consciousness, with recognition that we're all OneSelf, needing to live cooperatively in Love. Once established as a foundational vision, our approach becomes more spacious and powerful: We may be both clear and bold in naming all the various areas, structures and institutions formed from divisive consciousness over the centuries, and at the same time begin re-configuring these arenas from unity consciousness.
This greater vision of unity is the truest position of power, from which we may radically change ourSelves. If we take it as a guiding vision, there is no need – in fact, it is counter-productive – to give energy to opposition and aggressive denunciation of aberrant structures. Disclosure is not denunciation, but exposure to pure truth, from which the present delusions become self-evident, and one approaches dissolution with compassion. Compassion opens space for our passion to express itself in the most productive ways possible with positive focus on evolving the new possibilities so essential to creating a New Earth.
We are not saying that campaigns for education and disclosure are not important – they're essential – but only that they must arise from new-paradigm visioning with consciousness-in-action that makes unity-consciousness paramount. That will make their passion and clarity all the more effective.
To the extent that traditional religious and spiritual paths are willing to admit that they are not infallible, there may be an opening (from within their own institutions) for an integrated, inclusive, transparent and co-creative healthy evolutionary approach. Some of the great eastern teachers who taught in the West during the last century, for instance, were revolutionary in their own ways, but they were also carrying baggage from the patriarchal background of their traditions. The time has come to admit that re-calibration is needed. The sacred feminine must be honored and feast at the table. Evolving integral approaches that give new life and relevance to traditional teachings and practices must be embodied by women and men if we are to continue the rich wisdom and experiences offered.
Human evolution is accelerating — everything is accelerating. Global crises demand our full attention and the need for appropriate responses through a growing level of consciousness of those seeking to respond. Our spiritual approaches need to evolve, too. They need to catalyze the changes in consciousness that have taken place in the last few decades, into the social changes critical at this time: To bring us quickly to an emerging non-dual, integral consciousness – Self-realization – and pioneer new-ways-of-being to apply ancient wisdom of the heart into balanced co-creative collaboration and collective action for the salvation of humanity's higher evolution.
We need – as spiritual practitioners, guides, mentors, leaders and stewards – to evolve in 360-Ways. In doing so we honor, preserve and share what is perennially valid in the traditional ways, but we're also willing to leave behind what no longer serves the greater good in Love… and evolve new ways that do.
Love IS the fulcrum; join in this organic process for the higher evolution to a new earth.


Here's just a few observations from our first 3 weeks in the USA!
So far, two weeks in and nobody understands me when I say my name is Mike.
Sport. ALL DAY, EVERY DAY. I'm in heaven. Several games and sports on TV at once, and that's on regular TV, so I can only imagine there's more when you start paying. Every city and town has some sort of homage to whoever their local team is, both national league and college, and the pride and support for these teams shows up everywhere. I'd move here just to become a full-time sports fan. I'm also going to add here that the English soccer is shown live at a normal time of day, as opposed to the ridiculous times of midnight to 3am in Australia. It probably also helps that I prefer ice hockey, basketball, american football, and baseball over the rubbish sports we get back home in Oz.
Always nice to look to a different culture through the eyes of a guest. I love comments on Dutch culture from tourists or immigrants. Great to have someone open yours eyes for your own blindspots.
I would love to hear you say "Mike" the Aussie way! 🙂 And I agree on the tipping with the iPads at the airports.. that is just plain ridiculous!
Great post, Write the article on tipping. It's my pet peeve.
I think I will.. it's an interesting topic!
Yeah, I waitressed in the States a long time ago, – nothing posh, a family business where we waitresses were also short-order cooks, bussed (cleared) the tables and washed up. For a 95 cent bowl of soup the old bloke would leave us the 5 cents out of a dollar. Being an Aussie I felt quite at home with that. But then I made friends who worked for upmarket faces in upmarket places, – some of them actually paid to work there, because the tips were good. Now I'm wondering, – how much is that iPad paying for the privilege?
Sel and I have discussed the tipping quite a bit throughout our trip, in terms of working out multiple pros and cons, seeing how different restaurants do it differently, and comparing it how we do it back home.
I'm there with you on everything!
Best not even think about putting a Kiwi into the mix then!!!!!
Haha 🙂 I love a little caramel in my latte personally. I just find that Australians (every single one of them!) had an opinion about coffee. It is an interesting cultural thing (and I dig those!) I am curious about creamer; what is it?
You could just answer to Mark. Kinda like my kids – it doesn't matter what name I call you, if you are the only kid in the room you know I'm talking to you! Lol!
I loved this post!!! Keep posting about your travels. Can you imagine a British, Australian, or American South accent with Bangla! It's hilarious! Have a great day. We are heading back to America in 4 months.
Thank you!! Haha I do love hearing different accents, especially mixed accents. It makes me think my own accent is so bland… I'm thinking of adopting the accents I heard in Alabama. What do y'all think?
Having worked in the States for a number of years I have to agree with you about tipping….although our American cousins would have to go a long way to beat the Egyptians where it was common practice for them to loiter in the Gents toilets, reach inside the cubicle, tear off 2 sheets of toilet paper with one hand whilst holding out the second hand expecting a tip…bakheesh? Hah, not from this traveller!!!
Winner! That's even better than the kids at buddha monuments in Burma taking your shoes, putting them in a plastic bag, handing it back and then expecting a buck or two.
Loved this post. I'm British, husband is Aussie. We've lived in UK for years but moving to Aus in a couple of weeks. I'm looking forward to the culture change, new words and terminology. Oh and words I think I know but suddenly realise I can't pronounce properly. (Frangipani is a recent one that comes to mind!) Fun times.
Thank you Ashley 🙂 We leave for home on April 21st so still a couple more weeks! Where in the US is home?
Awesome! I grew up in LA but most recently lived in DC and Westchester County, NY. In Korea for a year so won't be stateside for awhile now and who knows where home will be when we return!
Well I'm glad it seems for the most part you enjoyed your visit. I have been actually yelled at twice, once in Boston, Massachusetts and then again in Dallas, TX for not offering a tip for unexceptional service. In Boston my hotel clerk had to intervene as I'm not as tiny and meek as I appear and the conversation was getting quite heated between my cab driver and myself.
A Dr. Pepper and Philly Cheesesteak, my all time favorite lunch.
Oh and I'm rubbish at recognizing all and any accents.
Well I missed an Aussie accent from some other tourists, so I'm going to apologise to all Americans for that one. If I can't pick an Aussie accent, what hope do y'all have!?
So far we haven't received any negative reactions to not leaving a tip – we've been good everywhere except we've forgotten this whole time to leave a tip for housekeeping at every hotel – luckily we haven't come back to a dead rat in the bed lol and yep, I'm still throwing down the Dr Pepper's as if it was my first day in the U.S!


The President. Let me simply say that this meeting is just a follow-on meeting. The Secretaries and Administrators here are able to help the victims of the hurricane, and I'm going to hear from them, each of them, as to what their Departments can do. We'll figure out how to follow up.
Over the weekend I was in touch with the manager of Homestead, city manager Alex Muxo; talked to the director of public safety of Dade County, Fred Taylor, and to Otis Wallace, one of the top officials in Florida City; and then General Sullivan called in with a good report as to what they're doing. And I must say I think progress is being made. And now we need to follow up with our Agencies and Departments in every way that we possibly can to assist the victims of this storm.
I might say for the benefit of the reporters here, as far as we're concerned, cooperation between the State and Federal is good. I've talked with General Sullivan about the coordination between the Guard and the regular Army, and he felt that that's in good shape, as did the director of public safety of Dade County. So I think real progress is being made, but there's still an awful lot of human suffering there. And what we all are going to try to do is continue to move forward as fast as we can to help alleviate that.
Q. Mr. President, some people have suggested that additional funding is going to be essential to help the people of southern Florida. Are you prepared to ask Congress now for additional funding?
The President. Yes. I think I made that statement the other day, that we are prepared. As soon as we get the estimates that are pouring in every day, why, we will go forward.
Q. Do you have any idea how much you'll be asking for?
The President. I don't have it now.
Note: The President spoke at 11:05 a.m. in the Cabinet Room at the White House. In his remarks, he referred to Gen. Gordon Sullivan, Army Chief of Staff in charge of the Federal forces involved in the recovery efforts in Florida.


The top dogs of South Africa had their moment of fame on the red carpet, which symbolised a year of hard work and dedication to achieve their ultimate goal – to be the best show or agility dogs in South Africa. These dogs and their owners competed in breed shows and agility competitions throughout the year and accumulated points for the Animaltalk Top Dog awards.
This annual red carpet event was once again a celebration of glitz and glamour, honouring the best of the best, the animal heroes and the brave dogs in the canine world. But there could only be one winner in each category.
We will soon launch the 2018 Animaltalk Top Dog Collector's edition that contains all the highlights of the day. Watch this space for more information. The March edition of Animaltalk will also contain the best highlights.


text:
\section{Introduction}

Recently, extracting textual information from natural scene images has become increasingly popular, due to the growing   demands of real-world applications (e.g., product search \cite{bai2017integrating}, image retrieval \cite{jaderberg2016reading}, and autonomous driving). Scene text detection, which aims at locating text in natural images, plays an important role in various text reading systems \cite{neumann2010method, epshtein2010detecting, wang2011end, bissacco2013photoocr, jaderberg2014deep,gomez2017textproposals, Busta_2017_ICCV, Li_2017_ICCV}.

\begin{figure}
\vspace{-2mm}

\begin{centering}
\includegraphics[scale=0.32]{imgs/overview5}
\par\end{centering}
\caption{The images in top row and bottom row are the predicted corner points and position-sensitive maps in top-left, top-right, bottom-right, bottom-left order, respectively.}
\label{img_overview}
\vspace{-5mm}
\end{figure}

Scene text detection is challenging due to both external and internal factors. The external factors come from the environment, such as noise, blur and occlusion, which are also major problems disturbing general object detection. The internal factors are caused by properties and variations of scene text. Compared with general object detection, scene text detection is more complicated because: 1) Scene text may exist in natural images with arbitrary orientation, so the bounding boxes can also be rotated rectangles or quadrangles; 2) The aspect ratios of bounding boxes of scene text vary significantly; 3) Since scene text can be in the form of characters, words, or text lines, algorithms might be confused when locating the boundaries.

\begin{figure*}
\vspace{-2mm}
\begin{centering}
\includegraphics[scale=0.25]{imgs/pipeline3}
\par\end{centering}
\caption{Overview of our method. Given an image, the network outputs corner points and segmentation maps by corner detection and position-sensitive segmentation. Then candidate boxes are generated by sampling and grouping corner points. Finally, those candidate boxes are scored by segmentation maps and suppressed by NMS.}
\label{img_pipeline}
\end{figure*}
In the past few years, scene text detection has been widely studied \cite{epshtein2010detecting, bissacco2013photoocr,yao2012detecting,jaderberg2014deep,tian2015text,zhang2016multi,Shi_2017_CVPR,tian2017wetext} and has achieved obvious progresses recently, with the rapid development of general object detection and semantic segmentation. Based on general object detection and semantic segmentation models, several well-designed modifications are made to detect text more accurately. Those scene text detectors can be split into two branches. The first branch is based on general object detectors (SSD \cite{liu2016ssd}, YOLO \cite{redmon2016you} and DenseBox \cite{huang2015densebox}), such as TextBoxes \cite{liao2017textboxes}, FCRN \cite{gupta2016synthetic}  and EAST \cite{Zhou_2017_CVPR} \emph{etc.}, which predict candidate bounding boxes directly. The second branch is based on semantic segmentation, such as \cite{zhang2016multi} and \cite{yao2016scene}, which generate segmentation maps and produce the final text bounding boxes by post-processing.

Different from previous methods, in this paper we combine the ideas of object detection and semantic segmentation and apply them in an alternative way. Our motivations mainly come from two observations: 1) a rectangle can be determined by corner points, regardless of the size, aspect ratio or orientation of the rectangle; 2) region segmentation maps can provide effective location information of text. Thus, we first detect the corner points (top-left, top-right, bottom-right, bottom-left, as shown in Fig. \ref{img_overview}) of text region rather than text boxes directly. Besides, we predict position-sensitive segmentation maps (shown in Fig. \ref{img_overview}) instead of a text/non-text map as in \cite{zhang2016multi} and \cite{yao2016scene}. Finally, we generate candidate bounding boxes by sampling and grouping the detected corner points and then eliminate unreasonable boxes by segmentation information. The pipeline of our proposed method is depicted in Fig. \ref{img_pipeline}.

The key advantages of the proposed method are as follows: 1) Since we detect scene text by sampling and grouping corner points, our approach can naturally handle arbitrary-oriented text; 2) As we detect corner points rather than text bounding boxes, our method can spontaneously avoid the problem of large variation in aspect ratio; 3) With position-sensitive segmentation, it can segment text instances well, no matter the instances are characters, words, or text lines; 4) In our method, the boundaries of candidate boxes are determined by corner points. Compared with regressing text bounding box from anchors ( \cite{liao2017textboxes, ma2017arbitrary}) or from text regions (\cite{Zhou_2017_CVPR,He_2017_ICCV}), the yielded bounding boxes are more accurate, particularly for long text.

We validate the effectiveness of our method on horizontal, oriented, long and oriented text as well as multi-lingual text from public benchmarks. The results show the advantages of the proposed algorithm in accuracy and speed. Specifically, the F-Measures of our method on ICDAR2015 \cite{karatzas2015icdar}, MSRA-TD500 \cite{yao2012detecting} and MLT \cite{MLT-Challenge} are \textbf{$84.3\%$}, \textbf{$81.5\%$} and \textbf{$72.4\%$} respectively, which outperform previous state-of-the-art methods significantly. Besides, our method is also competitive in efficiency. It can process more than \textbf{10.4} images (512x512 in size) per second.

The contributions of this paper are four-fold: (1) We propose a new scene text detector that combines the ideas of object detection and segmentation, which can be trained and evaluated end-to-end. (2) Based on position-sensitive ROI pooling \cite{dai2016r}, we propose a rotated position-sensitive ROI average pooling layer that can handle arbitrary-oriented proposals. (3) Our method can simultaneously handle the challenges (such as rotation, varying aspect ratios, very close instances) in multi-oriented scene text, which are suffered by previous methods. (4) Our method achieves better or competitive results in both accuracy and efficiency.

\section{Related Work}

\subsection{Regression Based Text Detection}

Regression based text detection has become the mainstream of scene text detection in the past two years. Based on general object detectors, several text detection methods were proposed and achieved substantial progress. Originating from SSD \cite{liu2016ssd}, TextBoxes \cite{liao2017textboxes} use "long" default boxes and "long" convolutional filters to cope with the extreme aspect ratios. Similarly, in \cite{ma2017arbitrary} Ma \emph{et al.} utilize the architecture of Faster-RCNN \cite{ren2015faster} and add rotated anchors in RPN to detect arbitrary-oriented scene text. SegLink \cite{Shi_2017_CVPR} predicts text segments and the linkage of them in a SSD style network and links the segments to text boxes, in order to handle long oriented text in natural scene. Based on DenseBox \cite{huang2015densebox}, EAST \cite{Zhou_2017_CVPR} regresses text boxes directly.

Our method is also adapted from a general object detector DSSD \cite{fu2017dssd}. But unlike the above methods that regress text boxes or segments directly, we propose to localize the positions of corner points, and then generate text boxes by sampling and grouping the detected corners.

\subsection{Segmentation Based Text Detection}

Segmentation based text detection is another direction of text detection. Inspired by FCN \cite{long2015fully}, some methods are proposed to detect scene text by using segmentation maps. In \cite{zhang2016multi}, Zhang \emph{et al.} first attempt to extract text blocks from a segmentation map by a FCN. Then they detect characters in those text blocks with MSER \cite{neumann2010method} and group the characters to words or text lines by some priori rules. In \cite{yao2016scene}, Yao \emph{et al.} use a FCN to predict three types of maps (text regions, characters, and linking orientations) of the input images. Then some post-processings are conducted to obtain text bounding boxes with the segmentation maps.

Different from the previous segmentation based text detection methods, which usually need complex post-processing, our method is simpler and clearer. In inference stage, the position-sensitive segmentation maps are used to score the candidate boxes by our proposed \textbf{Rotated Position-Sensitive Average ROI Pooling} layer.

\subsection{Corner Point Based General Object Detection}

Corner point based general object detection is a new stream of general object detection methods. In DeNet \cite{Tychsen-Smith_2017_ICCV}, Tychsen-Smith \emph{et al.} propose a corner detect layer and a sparse sample layer to replace RPN in a Faster-RCNN style two-stage model. In \cite{wang2017point}, Wang \emph{et al.} propose PLN (Point Linking Network) which regresses the corner/center points of bounding-box and their links using a fully convolutional network. Then the bounding boxes of objects are formed using the corner/center points and their links.

Our method is inspired by those corner point based object detection methods, but there are key differences. First, the corner detector of our method is different. Second, we use segmentation map to score candidate boxes. Third, it can produce arbitrary-oriented boxes for objects (text).

\subsection{Position-Sensitive Segmentation}

Recently, instance-aware semantic segmentation methods are proposed with position-sensitive maps. In \cite{dai2016instance}, Dai \emph{et al.} first introduce relative position to segmentation and propose InstanceFCN for instance segment proposal. In FCIS \cite{Li_2017_CVPR}, with the assistance of position-sensitive inside/outside score maps, Li \emph{et al.} propose an end-to-end network for instance-aware semantic segmentation.

We also adopt position-sensitive segmentation maps to predict text regions. Compared with the above-mentioned methods, there are three key differences: 1) We optimize the network with position-sensitive ground truth directly (detailed in Sec  \ref{sec_label}); 2)  Our position-sensitive maps can be used to predict text regions and score proposals simultaneously (detailed in Sec \ref{sec_scoring}), different from FCIS which uses two types of   position-sensitive maps (inside and outside); 3) Our proposed Rotated Position-Sensitive ROI Average Pooling can handle arbitrary-oriented proposals.

\begin{figure*}
\vspace{-3mm}

\begin{centering}
\includegraphics[scale=0.4]{imgs/network}
\par\end{centering}
\caption{Network Architecture. The network contains three parts: backbone, conner point detector and position-sensitive segmentation predictor. The backbone is adapted from DSSD \cite{fu2017dssd}. Conner point detectors are built on multiple feature layers (blocks in pink). position-sensitive segmentation predictor shares some features (pink blocks) with corner point detectors.}
\label{img_network}
\end{figure*}

\section{Network}

The network of our method is a fully convolutional network that plays the roles of feature extraction, corner detection and position-sensitive segmentation. The network architecture is shown in Fig. \ref{img_network}. Given an image, the network produces candidate corner points and segmentation maps.

\subsection{Feature Extraction}

The backbone of our model is adapted from a pre-trained VGG16 \cite{simonyan2014very} network and designed with the following considerations: 1) the size of scene text varies hugely, so the backbone must has enough capacity to handle this problem well; 2) backgrounds in natural scenes are complex, so the features should better contain more context. Inspired by the good performance achieved on those problem by FPN \cite{Lin_2017_CVPR} and DSSD \cite{fu2017dssd}, we adopt the backbone in FPN/DSSD architecture to extract features.

In detail, we convert the fc6 and fc7 in the VGG16 to convolutional layers and name them conv6 and conv7 respectively. Then several extra convolutional layers (conv8, conv9, conv10, conv11) are stacked above conv7 to  enlarge the receptive fields of extracted features. After that, a few deconvolution modules proposed in DSSD \cite{fu2017dssd} are used in a top-down pathway (Fig. \ref{img_network}). Particularly, to detect text with different sizes well, we cascade deconvolution modules with $256$ channels  from conv11 to conv3 (the features from conv10, conv9, conv8, conv7, conv4, conv3 are reused), and 6 deconvolution modules are built in total. Including the features of conv11, we name those output features $F_{3}, F_{4}, F_{7}, F_{8}, F_{9}, F_{10}$ and $F_{11}$ for convenience. In the end, the feature extracted by conv11 and deconvolution modules which have richer feature representations are used to detect corner points and predict position-sensitive maps.

\subsection{Corner Detection}

For a given rotated rectangular bounding box $R=(x,y,w,h,\theta)$, there are 4 corner points (top-left, top-right, bottom-right, bottom-left) and can be represented as two-dimensional coordinates $\{(x_{1},y_{1}),(x_{2},y_{2}),(x_{3},y_{3}),(x_{4},y_{4})\}$ in a clockwise direction. To expediently detect corner points, here we redefine and represent a corner point by a horizontal square $C=(x_{c},y_{c},ss,ss)$, where $x_{c},y_{c}$ are the coordinate of a corner point (such as $x_{1},y_{1}$ for top-left point) as well as  the center of the horizontal square. $ss$ is the length short side of the rotated rectangular bounding box $R$.

Following SSD and DSSD, we detect corner points with default boxes. Different from the manner in SSD or DSSD where each default box outputs the classification scores and offsets of the corresponding candidate box, corner point detection is more complex because there might be more than one corner points in the same location (such as a location can be the bottom-left corner and top-right corner of two boxes simultaneously). So in our case, a default box should output classification scores and offsets for $4$ candidate boxes corresponding to the $4$ types of corner points.

We adapt the prediction module proposed in \cite{fu2017dssd} to predict scores and offsets in two branches in a convolutional manner. In order to reduce the computational complexity, the filters of all convolutions are set to $256$. For an $m\times n$ feature map with $k$ default boxes in each cell, the "score" branch and "offset" branch output $2$ scores and $4$ offsets respectively  for each type of corner point of each default box. Here, $2$ for "score" branch means whether a corner point exists in this position. In total, the output channels of the "score" branch and the "offset" branch  are $k\times q\times2$ and $k\times q\times4$, where $q$ means the type of corner points. By default, $q$ is equal to 4.

In the training stage, we follow the matching strategy of default boxes and ground truth ones in SSD. To detect scene text with different sizes, we use default boxes of multiple sizes on multiple layer features. The scales of all default boxes are listed in Table \ref{tab_scales}. The aspect ratios of default boxes are set to $1$.

\begin{table*}
\vspace{-2mm}

\centering{}%
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
{\small{}layer} & {\small{}$F_{3}$} & {\small{}$F_{4}$} & {\small{}$F_{7}$} & {\small{}$F_{8}$} & {\small{}$F_{9}$} & {\small{}$F_{10}$} & {\small{}$F_{11}$}\tabularnewline
\hline
\hline
{\small{}scales} & {\small{}$4,8,6,10,12,16$} & {\small{}$20,24,28,32$} & {\small{}$36,40,44,48$} & {\small{}$56,64,72,80$} & {\small{}$88,96,104,112$} & {\small{}$124,136,148,160$} & {\small{}$184,208,232,256$}\tabularnewline
\hline
\end{tabular}
\caption{Scales of default boxes on different layers.}
\label{tab_scales}
\end{table*}

\subsection{Position-Sensitive Segmentation}

In the previous segmentation based text detection methods \cite{zhang2016multi, yao2016scene}, a segmentation map is generated to represent the probability of each pixel  belonging to text regions. However those text regions in score map always can not be separated from each other,  as a result of the overlapping of text regions and inaccurate predictions of text pixels. To get the text bounding boxes from the segmentation map, complex post-processing are conducted in \cite{zhang2016multi, yao2016scene}.

Inspired by InstanceFCN \cite{dai2016instance}, we use position-sensitive segmentation to generate text segmentation maps. Compared with previous text segmentation methods, relative positions are generated. In detail, for a text bounding box $R$, a $g\times g$ regular grid is used to divide the text bounding  box into multiple bins (\emph{i.e.}, for a $2\times2$ grid, a text region can be split into $4$ bins, that is top-left, top-right, bottom-right, bottom-left). For each bin, a segmentation map is used to determine whether the pixels in this map belong to this bin.

We build position-sensitive segmentation with corner point detection in a unified network. We reuse the features of {$F_{3}$, $F_{4}$, $F_{7}$, $F_{8}$, $F_{9}$} and build some convolutional blocks  on them follow the residual block architecture of corner point detection branch (Shown in Fig. \ref{img_network}). All outputs of those blocks are resized to the scale of $F_{3}$ by bilinear upsampling with the scale factors set to {$1$, $2$, $4$, $8$, $16$}. Then all those outputs with the same scale are added together to generate richer features. We further enlarge the resolution of fused features by two continuous \emph{Conv1x1-BN-ReLU-Deconv2x2} blocks and set the kernels of the last deconvolution layer to $g\times g$. So, the final position-sensitive segmentation maps have $g \times g$ channels and the same size as the input images. In this work, we set $g$ to 2 in default.

\section{Training and Inference}

\subsection{Training}
\subsubsection{Label Generation}
\label{sec_label}

For an input training sample, we first convert each text box in ground truth into a rectangle that covers the text box region with minimal area and then determine the relative position of $4$ corner points.

We determine the relative position of a rotated rectangle by the following rules: 1) the x-coordinates of top-left and bottom-left corner points must less than the x-coordinates of top-right and bottom-right corner points; 2) the y-coordinates of top-left and top-right corner points must less than the y-coordinates of bottom-left and bottom-right corner points.  After that, the original ground truth can  be represented as a rotated rectangle with relative position of corner points. For convenience, we term the rotated rectangle $R=\{P_{i}|i\in\{1,2,3,4\}\}$, where $P_{i} = (x_{i}, y_{i})$ are the corner points of the  rotated rectangle  in top-left, top-right, bottom-right, bottom-left order.

We generate the label of corner point detection and position-sensitive segmentation using  $R$. For corner point detection, we first compute the short side of $R$ and represent the $4$ corner points by  horizontal squares as shown in Fig. \ref{img_gt} (a).  For position-sensitive segmentation, we generate pixel-wise masks of text/non-text with $R$. We first initialize $4$ masks with the same scale as the input image and set all pixel value to $0$. Then we divide $R$ into four bins with a $2\times2$ regular grid and assign each bin to a mask, such as top-left bin to the first mask. After that, we set the value of  all pixels in those bins to $1$, as shown in Fig. \ref{img_gt} (b).

\begin{figure}

\begin{centering}
\includegraphics[scale=0.50]{imgs/gt1}
\par\end{centering}
\caption{Label generation for corner points detection and position-sensitive segmentation. (a) Corner points are redefined and represented by  squares (boxes in white, red, green, blue) with the side length set as the short side of text bounding box $R$ (yellow box). (b) Corresponding ground truth of $R$ in (a) for position-sensitive segmentation.}
\label{img_gt}
\end{figure}

\subsubsection{Optimization}
We train the corner detection and position-sensitive segmentation simultaneously. The loss function is defined as:
\begin{equation}
L=\frac{1}{N_{c}}L_{conf}+\frac{\lambda_{1}}{N_{c}}L_{loc} + \frac{\lambda_{2}}{N_{s}}L_{seg}
\end{equation}
Where $L_{conf}$ and $L_{loc}$ are the loss functions of the score branch for predicting confidence score  and the offset branch for localization in the module of corner point detection. $L_{seg}$ is the loss function of position-sensitive segmentation. $N_{c}$ is the number of positive default boxes, $N_{s}$ is the number of pixels in segmentation maps.  $N_{c}$ and $N_{s}$ are used to normalize the losses of corner point detection and segmentation. $\lambda_{1}$ and $\lambda_{2}$ are the balancing factors of the three tasks. In default, we set the $\lambda_{1}$ to 1 and $\lambda_{2}$  to 10.

We follow the matching strategy of SSD and train the score branch using Cross Entropy loss:

\begin{equation}
L_{conf}=CrossEntropy(y_{c}, p_{c})
\end{equation}

Where $y_{c}$ is the ground truth of all default boxes, 1 for positive and 0 otherwise. $p_{c}$ is the predicted scores. In consideration of the extreme imbalance between positive and negative samples, the category homogenization is necessary. We use the online hard negative mining proposed in \cite{shrivastava2016training} to balance training samples and set the ratio of positives to negatives to $1:3$.

For the offset branch, we regress the offsets relative to  default boxes as Fast RCNN  \cite{Girshick_2015_ICCV} and optimize them with Smooth L1 loss:
\begin{equation}
L_{loc}=SmoothL1(y_{l},p_{l})
\end{equation}

Where $y_{l}=(\triangle x, \triangle y, \triangle s_{s}, \triangle s_{s})$ is the ground truth of offset branch and $p_{l}=(\triangle\tilde{x}, \triangle\tilde{y}, \triangle\tilde{s_{s}}, \triangle\tilde{s_{s}})$ is the predicted offsets. The $y_{l}$ can be calculated by a default box $B=(x_{b}, y_{b}, ss_{b}, ss_{b})$ and a corner point box $C=(x_{c}, y_{c}, ss_{c}, ss_{c})$:

\begin{equation}
\triangle x=\frac{x_{b}-x_{c}}{ss_{b}}
\end{equation}

\begin{equation}
\triangle y=\frac{y_{b}-y_{c}}{ss_{b}}
\end{equation}

\begin{equation}
\triangle ss=\log(\frac{ss_{b}}{ss_{c}})
\end{equation}

We train position-sensitive segmentation by minimizing the Dice loss \cite{milletari2016v}:
\begin{equation}
L_{seg}=1-\frac{2y_{s}p_{s}}{y_{s} + p_{s}}
\end{equation}
Where $y_{s}$ is the label of position-sensitive segmentation and $p_{s}$ is the prediction of our segmentation module.

\subsection{Inference}
\subsubsection{Sampling and Grouping}
In inference stage, many corner points are yielded with the predicted location, short side and confidence score. Points with high score (great than 0.5 in default) are kept. After NMS, 4 corner point sets are composed  based on relative position information.

We generate the candidate bounding boxes by sampling and grouping the predicted corner points.  In theory, a rotated rectangle can be constructed by two points and a side perpendicular to the line segment made up by the two points. For a predicted point, the short side is known, so we can form a
rotated rectangle by sampling and grouping two corner points in corner point sets arbitrarily, such as (top-left, top-right), (top-right, bottom-right), (bottom-left, bottom-right) and (top-left, bottom-left) pairs.

Several priori rules are used to filter unsuitable pairs: 1) the relative positional relations can not be violated, such as the x-coordinate of top-left point must less than that of top-right point in (top-left, top-right) pair; 2) the shortest side of the constructed rotated rectangle must be greater than a threshold (the default is 5); 3) the predicted short sides $ss_{1}$ and $ss_{2}$ of the two points in a pair must satisfy:
\begin{equation}
\frac{\max(ss_{1},ss_{2})}{\min(ss_{1},ss_{2})}\leq1.5
\end{equation}

\subsubsection{Scoring}
\label{sec_scoring}
A large number of candidate bounding boxes can be generated after sampling and
grouping corner points. Inspired by InstanceFCN\cite{dai2016instance} and RFCN \cite{dai2016r}, we score the candidate bounding boxes by the position-sensitive segmentation maps. The  processes are shown in Fig. \ref{img_gt}.

To handle the rotated text bounding boxes, we adapt the Position-Sensitive ROI pooling layer in \cite{dai2016r} and propose \textbf{Rotated Position-Sensitive ROI Average pooling layer}. Specifically, for a rotated box, we first split the box into $g\times g$ bins. Then we generate a rectangle for each bin with the minimum area to cover the bin. We loop over all pixels in the minimum rectangle and calculate mean value of all pixels which in the bin. In the end, the score of a rotated bounding box is obtained by  averaging the means of $g\times g$ bins. The specific processes are shown in Algorithm \ref{algo_rps}.

\begin{figure}
\begin{centering}
\includegraphics[scale=0.25]{imgs/scoring}
\par\end{centering}
\caption{Overview of the scoring process. The yellow boxes in (a) are candidate boxes. (b) are predicted segmentation maps. We generate instance segment (c) of candidate boxes by assembling the segmentation maps as \cite{dai2016instance}. Scores are calculated by averaging the instance segment regions. }
\label{img_gt}
\vspace{-3mm}
\end{figure}

\begin{algorithm}[t]
\caption{Rotated Position-Sensitive ROI Average Pooling}
\hspace*{0.02in} {\bf Input:}
rotated bounding box $B$, $g\times g$ regular grid $G$, Segmentation maps $S$
\begin{algorithmic}[1]
\State Generating $Bins$ by spitting $B$ with $G$.
\State $M\leftarrow 0$, $i\leftarrow 0$
\For{$i$ in $range(g\times g)$}
	\State $bin\leftarrow Bins[i]$, $C\leftarrow 0$, $P\leftarrow 0$, 	\State $R \leftarrow MiniRect(bin)$
	\For{$pixel$ in $R$}
		\If{$pixel$ in $bin$}
    		\State $C\leftarrow C + 1$, $P\leftarrow P + G[i][pixel].value$
		\EndIf
	\EndFor
	\State $M\leftarrow M + \frac{P}{C}$
\EndFor
\State $score \leftarrow \frac{M}{g\times g}$
\State \Return score
\end{algorithmic}
\label{algo_rps}
\end{algorithm}

The candidate boxes with low score will be filtered out. We set the threshold $\tau$ to 0.6 by default.

\section{Experiments}
To validate the effectiveness of the proposed method, we conduct experiments on five public datasets: ICDAR2015, ICDAR2013, MSRA-TD500, MLT, COCO-Text, and compare with other state-of-the-art methods.

\subsection{Datasets}
\textbf{SynthText} \cite{gupta2016synthetic} is a synthetically generated dataset which consists of about 800000 synthetic images.   We use the dataset with word level labels to pre-train our model.

\textbf{ICDAR2015} is a dataset proposed in the Challenge 4 of the 2015 Robust Reading Competition \cite{karatzas2015icdar} for incidental scene text detection. There are 1000 images for training  and 500 images for testing with annotations labeled as word level quadrangles.

\textbf{ICDAR2013} is a dataset proposed in the Challenge 2  of the 2013 Robust Reading Competition \cite{karatzas2013icdar} focuses on horizontal text in scene. It contains 229 images for training and 233 images for testing.

\textbf{MSRA-TD500} \cite{yao2012detecting} is a dataset collected for detecting arbitrary-oriented long text lines. It consists of 300 training images and 200 test images with text line level annotations.

\textbf{MLT}  is a dataset that proposed on ICDAR2017 Competition \cite{MLT-Challenge} and  focuses on the multi-oriented, multi-script and multi-lingual aspects of scene text. It consists of 7200 training images, 2000 validation images and 9000 test images.

\textbf{COCO-Text} \cite{veit2016coco} is a large scale scene text dataset which comes from the MS COCO dataset \cite{lin2014microsoft}. There are 63686 images are annotated and two versions of annotations and splits (V1.1 and V1.4) are released by the official. Previous methods are all evaluated on V1.1 and the new V1.4 are used on ICDAR2017 Competition \cite{COCO-Text-Challenge}.
\subsection{Implementation Details}
\textbf{Training} Our model is pre-trained on SynthText then finetuned on other datasets (except COCO-Text). We use Adam \cite{kingma2014adam} to optimize our model with the learning rate fixed to $1e-4$. In pre-train stage, we train our model on SynthText for one epoch. During finetuning stage, the number of iterations are decided by the sizes of datasets.

\textbf{Data Augmentation} We use the same way of data augmentation as SSD. We randomly sample a patch from the input image  in the manner of SSD, then resize the sampled patch to $512\times512$.

\textbf{Post Processing} NMS is the only  post processing step of our method. We set the threshold of NMS to $0.3$.

Our method is implemented in PyTorch \cite{PyTorch}. All the experiments are conducted on a regular workstation (CPU: Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz; GPU:Titan Pascal; RAM: 64GB). We train our model with the batch size of 24 on $4$ GPUs in parallel and evaluate our model on 1 GPU with batch size set as $1$.

\begin{figure*}

\noindent \begin{centering}
\includegraphics[scale=0.28]{imgs/results1}
\par\end{centering}
\caption{Examples of detection results. From left to right in columns: ICDAR2015, ICDAR2013, MSRA-TD500, MLT, COCO-Text.}
\label{img_results}
\end{figure*}

\subsection{Detecting Oriented Text}

We evaluate our model on the ICDAR2015 dataset to test its ability of arbitrarily oriented text detection. We finetune our model another 500 epochs on the datasets of ICDAR2015 and ICDAR2013. Note that, to detect vertical text better, in the last 15 epochs, we randomly rotate the sampled patches by $90$ degree or $-90$ degree with the probability  of $0.2$. In testing, we set $\tau$ to 0.7 and  resize the input images to $768\times1280$. Following \cite{Zhou_2017_CVPR,Hu_2017_ICCV,He_2017_ICCV}, we also evaluate our model on ICDAR2015 with multi-scale inputs, $\{512\times512, 768\times768, 768\times1280, 1280\times1280\}$ in default.

We compare our method with other state-of-the-art methods and list all the results in Table \ref{tab_icdar2015}. Our method outperforms the previous methods by a large margin. When tested at single scale, our method achieves the F-measure of $80.7\%$, which surpasses all competitors \cite{zhang2016multi,tian2016detecting,yao2016scene,Shi_2017_CVPR,Zhou_2017_CVPR,SSTD} . Our method achieves $84.3\%$ in F-measure with multi-scale inputs, higher than the current best one \cite{He_2017_ICCV} by $3.3\%$.

To explore the gain between our method which detects corner points and the method which regresses text boxes directly, we train a network named "baseline" in Table. \ref{tab_icdar2015} using the same settings as our method. The baseline model consists of the same backbone as our method and the prediction module in SSD/DSSD. With slight time cost, our method boost the accuracy greatly ($53.3\%$ \emph{vs} $80.7\%$).

\begin{table}
\small

\begin{centering}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Method} & \textbf{Precision} & \textbf{Recall} & \textbf{F-measure} & \textbf{FPS} \tabularnewline
\hline
\hline
Zhang \emph{et al.} \cite{zhang2016multi} & 70.8 & 43.0 & 53.6 & 0.48 \tabularnewline
\hline
CTPN \cite{tian2016detecting} & 74.2  & 51.6  & 60.9 & 7.1 \tabularnewline
\hline
Yao \emph{et al.} \cite{yao2016scene} & 72.3  & 58.7  & 64.8 & 1.61 \tabularnewline

\hline
SegLink \cite{Shi_2017_CVPR} & 73.1  & \textbf{76.8} & 75.0 & - \tabularnewline
\hline
EAST \cite{Zhou_2017_CVPR} & 80.5  & 72.8 & 76.4 & 6.52\tabularnewline
\hline
SSTD \cite{SSTD} & 80.0  & 73.0 & 77.0 & \textbf{7.7} \tabularnewline

\hline
\textbf{baseline} & 66.0 & 44.7 & 53.3 & 4.5 \tabularnewline

\hline
\textbf{ours} & \textbf{94.1} & 70.7 & \textbf{80.7} & 3.6 \tabularnewline
\hline
\hline
EAST $^*$ $^\text{\dag}$ \cite{Zhou_2017_CVPR}& 83.3  & 78.3 & 80.7 & - \tabularnewline
\hline
WordSup $^*$ \cite{Hu_2017_ICCV} & 79.3  & 77.0  & 78.2 & \textbf{2} \tabularnewline
\hline
He \emph{et al.} $^*$ $^\text{\dag}$ \cite{He_2017_ICCV} & 82.0  &  \textbf{80.0} & 81.0 & 1.1 \tabularnewline
\hline
\textbf{ours}$^*$ & \textbf{89.5} & 79.7 & \textbf{84.3} & 1 \tabularnewline
\hline
\end{tabular}
\par\end{centering}
\caption{Results on ICDAR2015. $^*$ means multi-scale, $^\text{\dag}$ stands for the base net of the model is not VGG16.}
\label{tab_icdar2015}
\end{table}

\subsection{Detecting Horizontal Text}

We evaluate the ability of our model to detect horizontal text on ICDAR2013 dataset. We further train our model on ICDAR2013 dataset for 60 epochs on the basis of the finetuned ICDAR2015 model. In testing, the input images are resized to $512\times512$. We also use multi-scale inputs to evaluate our model.

The  results are listed in Table \ref{tab_icdar2013} and mostly are reported with the "Deteval" evaluation protocol. Our method achieves very competitive results. When tested at single scale, our method achieves the F-measure of $85.8\%$, which is slightly lower than the highest result. Besides, our method can run at 10.4 FPS, faster than most methods. For multi-scale evaluation, our method achieves the F-measure of $88.0\%$, which is also  competitive compared with other methods.

\begin{table}

\footnotesize
\begin{centering}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Method} & \textbf{Precision} & \textbf{Recall} & \textbf{F-measure} & \textbf{FPS} \tabularnewline
\hline
\hline
Neumann \emph{et al.} \cite{neumann2015efficient} & 81.8 & 72.4 & 77.1 & 3  \tabularnewline

\hline
Neumann \emph{et al.} \cite{neumann2016real} & 82.1 & 71.3  & 76.3  & 3 \tabularnewline

\hline
Fastext \cite{busta2015fastext} & 84.0 & 69.3 & 76.8 & 6  \tabularnewline
\hline
Zhang \emph{et al.} \cite{zhang2015symmetry} & 88.0 & 74.0 & 80.0 & 0.02\tabularnewline
\hline
Zhang \emph{et al.} \cite{zhang2016multi} & 88.0  & 78.0  & 83.0 & 0.5 \tabularnewline

\hline
Yao \emph{et al.} \cite{yao2016scene} & 88.9  & 80.2  & 84.3 & 1.61 \tabularnewline
\hline
CTPN \cite{tian2016detecting} & 93.0  & 83.0  & \textbf{88.0} & 7.1 \tabularnewline

\hline
TextBoxes \cite{liao2017textboxes} & 88.0  & 74.0  & 81.0 & 11 \tabularnewline

\hline
SegLink \cite{Shi_2017_CVPR} & 87.7  & 83.0 & 85.3 & \textbf{20.6}  \tabularnewline

\hline
SSTD \cite{SSTD} & 89.0  & \textbf{86.0} & \textbf{88.0} & 7.7 \tabularnewline

\hline
\textbf{ours} & \textbf{93.3} & 79.4 & 85.8 & 10.4 \tabularnewline
\hline
\hline
FCRN $^*$ \cite{gupta2016synthetic} & 92.0  & 75.5  & 83.0  & 0.8 \tabularnewline
\hline
TextBoxes $^*$ \cite{liao2017textboxes} & 89.0  & 83.0  & 86.0 & 1.3 \tabularnewline
\hline
He \emph{et al.} $^*$ $^\text{\dag}$ \cite{He_2017_ICCV} & 92.0  &  81.0 & 86.0 & 1.1  \tabularnewline
\hline
WordSup $^*$ \cite{Hu_2017_ICCV} & \textbf{93.3}  & \textbf{87.5}  &  \textbf{90.3} & \textbf{2} \tabularnewline
\hline
\textbf{ours}$^*$ & 92.0 & 84.4 & 88.0 & 1\tabularnewline
\hline
\end{tabular}
\par\end{centering}
\caption{Results on ICDAR2013. $^*$ means multi-scale, $^\text{\dag}$ stands for the base net of the model is not VGG16. Note that, the methods of the top three lines are evaluated under the "ICDAR2013" evaluation protocol.}
\label{tab_icdar2013}
\end{table}

\subsection{Detecting Long Oriented Text Line}

On MSRA-TD500, we evaluate the performance of our method for detecting long and multi-lingual text lines. HUST-TR400 is also used as training data as the MSRA-TD500 only contains 300 training images. The model is initialized with the model pre-trained on SynthText and then finetuned another 240 epochs. In test stage, we input the images with the size $768\times 768$ and set $\tau$ to 0.65.

As shown in Table \ref{tab_msra}, our method surpasses all the previous methods by a large margin. Our method achieves state-of-the-art performances both in recall, precision and F-measure ($87.6\%$, $76.2\%$ and $81.5\%$), and much better than the previous best result ($81.5\%$ \emph{vs.} $77.0\%$). That means our method is more capable than other methods of detecting arbitrarily oriented long text.

\begin{table}

\small

\begin{centering}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Method} & \textbf{Precision} & \textbf{Recall} & \textbf{F-measure} &\textbf{FPS} \tabularnewline
\hline
\hline
TD-ICDAR \cite{yao2012detecting} & 53.0 & 52.0 & 50.0 & -\tabularnewline

\hline
TD-Mixture \cite{yao2012detecting} & 63.0 & 63.0 & 60.0 & -\tabularnewline

\hline
Kang \emph{et al.} \cite{kang2014orientation} & 71.0 &  62.0 & 66.0 & -\tabularnewline

\hline
Zhang \emph{et al.} \cite{zhang2016multi} & 83.0 & 67.0 & 74.0 & 0.48\tabularnewline

\hline
Yao \emph{et al.} \cite{yao2016scene} & 76.5  & 75.3  & 75.9 &  1.61\tabularnewline

\hline
EAST \cite{Zhou_2017_CVPR}  & 81.7  & 61.6 & 70.2 & 6.52 \tabularnewline
\hline
EAST  $^\text{\dag}$ \cite{Zhou_2017_CVPR} & 87.3  & 67.4 & 76.1 & \textbf{13.2} \tabularnewline
\hline
SegLink \cite{Shi_2017_CVPR} & 86.0  & 70.0 & 77.0 & 8.9 \tabularnewline
\hline
He \emph{et al.} $^\text{\dag}$ \cite{He_2017_ICCV} & 77.0  &  70.0 & 74.0 & 1.1 \tabularnewline
\hline
\textbf{ours} & \textbf{87.6} & \textbf{76.2} & \textbf{81.5} & 5.7 \tabularnewline
\hline
\end{tabular}
\par\end{centering}
\caption{Results on MSRA-TD500. $^\text{\dag}$ stands for the base net of the model is not VGG16.}
\label{tab_msra}
\end{table}

\subsection{Detecting Multi-Lingual Text}

We verify the ability of our method to detect multi-lingual text on MLT. We finetune about 120 epochs on the model pre-trained on SynthText. When testing in single scale, the sizes of images are set as  $768 \times 768$. We evaluate our method online and compare with some public results on the leaderboard \cite{MLT-Challenge}. As shown in Table \ref{tab_mlt}, our method outperforms all competing methods by at least $3.1\%$.

\begin{table}

\small

\begin{centering}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Method} & \textbf{Precision} & \textbf{Recall} & \textbf{F-measure}  \tabularnewline
\hline
\hline
TH-DL \cite{MLT-Challenge} & 67.8 & 34.8 & 46.0 \tabularnewline
\hline
SARI\_FDU\_RRPN\_V1 \cite{MLT-Challenge} &71.2 & 55.5 & 62.4 \tabularnewline
\hline
Sensetime OCR \cite{MLT-Challenge} &56.9 & 69.4 & 62.6  \tabularnewline
\hline
SCUT\_DLVClab1 \cite{MLT-Challenge} &80.3  &54.5  &65.0  \tabularnewline

\hline
e2e\_ctc01\_multi\_scale \cite{MLT-Challenge} & 79.8 & 61.2 & 69.3 \tabularnewline

\hline
\textbf{ours} & \textbf{83.8} & 55.6 & 66.8  \tabularnewline
\hline
\textbf{ours}$^*$ & 74.3 & \textbf{70.6} & \textbf{72.4}  \tabularnewline
\hline
\end{tabular}
\par\end{centering}
\caption{Results on MLT. $^*$ means multi-scale.}
\label{tab_mlt}
\vspace{-3mm}
\end{table}

\subsection{Generalization Ability}

To evaluate the generalization ability of our model, we test it on COCO-Text using the model finetuned on ICDAR2015. We set the test image size as $768\times 768$. We use the annotations (V1.1) to compare with other methods, for the sake of fairness. The results are shown in Table \ref{tab_coco}. \textbf{Without training}, on COCO-Text, our method achieves an F-measure of $42.5\%$, better than competitors.

Besides, we also evaluate our model on the ICDAR2017 Robust Reading Challenge on COCO-Text \cite{COCO-Text-Challenge} with the annotations V1.4. The results are reported in Table \ref{tab_coco}. Among all the public results in leaderboard \cite{COCO-Text-Challenge}, our method ranks the first. Especially when the threshold of iou is set to 0.75, the result that our method exceeds others in a large margin shows it can detect text more accurately.

\begin{table}
\small

\begin{centering}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Method} & \textbf{Precision} & \textbf{Recall} & \textbf{F-measure}  \tabularnewline
\hline
\hline
Baseline A  \cite{veit2016coco} & 83.8 & 23.3 &  36.5\tabularnewline

\hline
Baseline B \cite{veit2016coco} & \textbf{89.7}  & 10.7  & 19.1 \tabularnewline

\hline
Baseline C \cite{veit2016coco} & 18.6  & 4.7 & 7.5 \tabularnewline
\hline
Yao \emph{et al.} \cite{yao2016scene} & 43.2  & 27.1 & 33.3 \tabularnewline
\hline
EAST \cite{Zhou_2017_CVPR}   & 50.4  & \textbf{32.4} & 39.5  \tabularnewline
\hline
WordSup \cite{Hu_2017_ICCV} & 45.2  & 30.9 & 36.8 \tabularnewline
\hline
SSTD \cite{SSTD} & 46.0  &  31.0 & 37.0 \tabularnewline
\hline
\textbf{ours}    &69.9  & 26.2 & 38.1    \tabularnewline
\hline

\textbf{ours}$^*$ & 61.9 & \textbf{32.4} & \textbf{42.5}  \tabularnewline

\hline
\multicolumn{4}{|c|}{COCO-Text Challenge (IOU 0.5)}\tabularnewline
\hline
UM \cite{COCO-Text-Challenge} & 47.6   & \textbf{65.5}  & 55.1   \tabularnewline
\hline
TDN\_SJTU\_v2 \cite{COCO-Text-Challenge} & 62.4   & 54.3  & 58.1   \tabularnewline

\hline
Text\_Detection\_DL \cite{COCO-Text-Challenge} & 60.1 & 61.8  & 61.4   \tabularnewline

\hline
\textbf{ours} & \textbf{72.5} & 52.9 & 61.1  \tabularnewline

\hline
\textbf{ours}$^*$ & 62.9 & 62.2 & \textbf{62.6}  \tabularnewline

\hline
\multicolumn{4}{|c|}{COCO-Text Challenge (IOU 0.75)}\tabularnewline
\hline
Text\_Detection\_DL \cite{COCO-Text-Challenge} & 25.2 & 25.5  & 25.4  \tabularnewline
\hline
UM \cite{COCO-Text-Challenge} & 22.7   & 31.2  & 26.3   \tabularnewline

\hline
TDN\_SJTU\_v2 \cite{COCO-Text-Challenge} & 31.8   & 27.7  &  29.6   \tabularnewline

\hline
\textbf{ours} & \textbf{40.0} & 30.0 & 34.6  \tabularnewline

\hline
\textbf{ours}$^*$ & 35.1 & \textbf{34.8} & \textbf{34.9}  \tabularnewline

\hline
\end{tabular}
\par\end{centering}
\caption{Results on COCO-Text.  $^*$ means multi-scale.}
\label{tab_coco}
\vspace{-3mm}
\end{table}

\vspace{-0.1cm}

\subsection{Limitations}
\vspace{-0.1cm}
One limitation of the proposed method is that when two text instances are extremely close, it may predict the two instances as one (Fig. \ref{img_limit}), since the position-sensitive segmentation might fail. Besides, the method is not good at detecting curved text (Fig. \ref{img_limit}), as there are few curved samples in the training set.

\begin{figure}

\begin{centering}
\includegraphics[scale=0.25]{imgs/limitations2}
\par\end{centering}
\caption{Failure cases of our method. The boxes in green are ground truth. The red boxes are our predictions.}
\label{img_limit}
\vspace{-4mm}
\end{figure}

\vspace{-0.1cm}
\section{Conclusion}
\vspace{-0.1cm}

In this paper, we have presented a scene text detector that localize text by corner point detection and position-sensitive segmentation. We evaluated it on several public benchmarks focusing on oriented, horizontal, long oriented and multi-lingual text. The superior performances demonstrate the effectiveness and robustness of our method. In the future, we are interested in constructing an end-to-end OCR system based on the proposed method.

\vspace{-1mm}
{
\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


text:
Phoenicians *
The Phoenicians or Canaanites are linguistically regarded as Semitic people, who among their many achievements are credited with giving us our alphabet (without vowels). Both Strabo and Herodotus claim that they originally came from Bahrain(p), but this origin is denied by the phoenicia.org website(q). The correctness of these two ancient writers has been heavily criticised(r).
Nevertheless, Dhani Irwanto in a 2019 article(ab) on his website insists that the Phoenicians originated somewhere in the Indian Ocean. Irwanto suggests "that perhaps the most significant contribution of the Phoenicians was an alphabetic writing system that became the root of the Western alphabets." However, Irwanto has gone further and proposed that the Phoenicians also influenced the development of at least three of the scripts of South Sumatera in Indonesia!
The widespread idea that Phoenician writing inspired the development of European alphabets, such as that of the Greeks, conflicts with the discovery of the Dispilo Tablet(ad) in Greece which has pushed back the use of writing in Greece to around 5200 BC, which is long before the people recognised as Phoenician emerged in the 2nd millennium BC(ac)!
A recent essay explicitly claims that there was never any such entity as 'Phoenicia'! It also charts the manner in which 'Phoenicians' have been arbitrarily claimed as ancestors by distant nations, having been "enlisted in support of the nationalist histories of Lebanon, Britain, and Ireland, and in some cases seriously distorted by them. Despite claims by various partisans of Lebanese, British and Irish nationalism to enlist the Phoenicians as their ancient progenitor, the Phoenicians never existed as a self-conscious community, let alone a nascent nation." (y)
The Phoenicians flourished during the 1st and 2nd millennia BC. The late Joseph Robert Jochmans has suggested(c) that similarities between Phoenician names and those of the sons of Poseidon are more than coincidental. The descendants of the Phoenicians are still to be found in great numbers in modern Lebanon as well as elements of the Phoenician language. Contrary to popular belief the Maltese language is more related to Phoenician than Arabic(g). Similarly, in a mountainous and isolated northeast corner of Asia Minor, its people still speak Greek in a dialect known as Romeyka(l). Dr Ioanna Sitaridou of Queen's College, Cambridge explains that 'Although Romeyka can hardly be described as anything but a Modern Greek dialect, it preserves an impressive number of grammatical traits that add an Ancient Greek flavor to the dialect's structure – traits that have been completely lost from other Modern Greek varieties.'
A more radical view of the Phoenicians has been expressed by Professor Josephine Quinn(o) who declared "the Phoenicians never existed as a self-conscious community, let alone a nascent nation." In a lengthy article, she suggests that "''Phoenician' was just a generic label invented by ancient Greek authors for the Levantine sailors they encountered in their own maritime explorations. Although some of these Greek writers entertain a mild stereotype of these Phoenicians as rather cunning or tricksy, they never use the term as a description of a distinct ethnocultural community."
The Phoenicians have been frequently identified as the Atlanteans of Plato's narrative. Peter Dawkins' Zoence Academy website has the following logic-stretching gem – "Atlas also is known by other names, such as Enoch or The Phoenix, (hence Atlantis is Phoenicia, the land of the Phoenix)(v).
Keith Hunt noted on his website that "Prof. George Rawlinson (1812-1902), in his "Story of Phoenicia," tells us that Phoenicia derived its name from the forests of date or Phoenix palms which grew there in great luxuriance. So far so good; but whence did the Phoenix palm derive its name? Horapollo says: "A palm branch was the symbol of the Phoenix." Yes, but what or who was the Phoenix? Sanchomathon, the Phoenician writer, states that "Phoenix was the first Phoenician." Phoenix, then, was a man. Now, the word Phoenix is the Greek form of the Egyptian term "Pa-Hanok," the house of Enoch. In Hebrew Enoch also is Hanok. Thus the mystery of that ancient race is solved: they were the sons and descendants of Enoch and of Noah and his three sons, who after the Flood started their westward march. Their descendants have kept it up since, settled, first north of the Persian Gulf in the bushlands of Mesopotamia, where they found a dusky race in occupation of the land, the ancient Sumerians, and from thence towards the Mediterranean." (w)
James Nienhuis has recently identified Canaanites as Atlanteans(m)! The supporters of a Bronze Age date for the invasion of the Atlanteans see in the Phoenicians the powerful far-flung maritime civilisation described by Plato. However, this identification is in conflict with Plato's statement that Atlantis or its influence extended as far as Tyrrhenia and Libya, whereas the Phoenicians had their original base further east in the region of modern Lebanon and Israel. It also runs counter to Plato's clear account of the Atlanteans attacking from their bases in the Central Mediterranean (Tim.25b & Crit.114c).
The Phoenicians were never unilaterally at war with Greece and/or Egypt, but their successors, the Carthaginians, whose main military campaigns were directed against the Roman Empire, did clash with the Greeks in Sicily.
It is accepted that the Phoenician commercial empire began with the three cities of Tyre, Sidon, and Byblos. They expanded with the establishment of trading settlements along the Mediterranean coast of North Africa usually separated by a day's rowing – somewhere between 30 to 60 miles.
It is claimed that the Phoenicians together with the Egyptians had an influence on the development of the Minoan culture(e).
Jonas Bergman recently presented a paper to the 2005 Melos Atlantis Conference on the subject of a Phoenician association with the Atlantis story. He outlined how Plato's description of Atlantis was similar to the western colonies of the Phoenicians.
"Roderic O'Flaherty (Ruaidrhí Ó Flaithbheartaigh) was the first Irish scholar to suggest in his influential work Ogygia (1685) that the Phoenicians formed part of Irish ancestry. In the 18th century, O'Flaherty's theory of the Phoenicians as progenitors of the Irish became very popular among the Protestant Ascendancy as well as Gaelic intellectuals."(y)
Joaquín Lorenz Villanueva (1757-1837) was a Spanish historian, who moved to Dublin in later life, where he wrote Ibernia Phœnicea [1664], which was an attempt to prove that Ireland had been colonised by the Phoenicians. This was translated into English and published by Henry O'Brien in 1833 as Phoenician Ireland [1663].
Phoenicians in America
Some German writers in the 19th century such as Robert Prutz and later Jakob Kruger have advocated the idea that Phoenicians had discovered America, where he also placed Atlantis. However, in spite of the fact that there is widespread support for this concept and the even more extreme claim of Phoenicians in Australia, a Lebanese website (now offline), in the original home of Phoenicia, discounted all such claims for lack of evidence. Nevertheless, attention-seeking Rex Gilroy persists in promoting the idea of Phoenicians in Australia(h).
Noteworthy is the fact that the world's only traditionally-built replica of a Phoenician ship made the trans-Atlantic crossing successfully, landing in Fort Lauderdale in February 2020(u).
A paper by Christian C. Karam, who believes that Atlantis had been located in the Atlantic has expanded on the idea of a Phoenician presence in Brazil three thousand years ago(n).
In 1886, the American novelist Ann Eliza Smith (1819-1905) published a fantasy novel(j), Atla, that tells the tale of the discovery of the Atlantis civilization by the Phoenicians.
In 1889, Enrique Onffroy de Thoron proposed[0901] that Atlantis had been Phoenician and situated in America. Indeed, claims still persist that the Phoenicians did reach South America(f). However, Onffroy was not the first to suggest this, as he was preceded by Robertus Comtaeus Nortmannus as early as 1644 and Georg Horn in 1652.
Paul Gallez (1920-2007) was a Belgian-born cartographer, historian and linguist. Atlantisforschung noted that "On the occasion of the celebrations of the 500th anniversary of the alleged discovery of America by Christopher Columbus (the 'Columbus Day' of the year 1992), Gallez wrote and published in 1996 a whole series of articles about much earlier voyages of discovery, e.g. by Phoenicians, Egyptians, Chinese and Vikings in the journal La Nueva Provincia , in which he also considered proto-Semitic influences in pre-Columbian America"(ah).
Arguably, the best-known exponent of the 'Phoenicians in America' school of thought was Bernardo Silva Ramos(i). In the 1930s he studied inscriptions at Pedra de Gavea and concluded that they were Phoenician and offered a translation that read "Tyre, Phoenicia, Badezir, Firstborn of Jethbaal". This supposedly refers to a ruler of Phoenicia named Baal-Eser I, who ruled Tyre in the mid-9th century, c. 850 BC. Jacques de Mahieu decided that the inscriptions were not Phoenician but, in keeping with his Nazi background, they were Nordic runes!(ae) Otto Muck also bought into the Phoenicians in Brazil idea and according he accepted the translation of Bernardo Silva Ramos(af).
Paul Gaffarel wrote a number of papers between 1875 and 1890 on pre-Columbian trans-Atlantic voyagers from Europe. He presented a 38-page paper on the Phoenicians' voyages to America to the Congrès International des Américanistes in Nancy (France) in 1875. Other essays concerned the Vikings and ancient Irish(ag).
Nevertheless, John Denison Baldwin, writing in the late 19th century, was highly critical of the Phoenicians being early colonisers in America and after reviewing the arguments in favour of the idea, he concluded that "if it were true that the civilization found in Mexico and Central America came from people of the Phoenician race, it would be true also that they built in America as they never built anywhere else, that they established a language here radically unlike their own, and that they used a style of writing totally different from that which they carried into every other region occupied by their colonies. All the forms of alphabetical writing used at present in Europe and Southwestern Asia came directly or indirectly from that anciently invented by the race to which the Phoenicians belonged, and they have traces of a common relationship that can easily be detected. Now the writing of the inscriptions at Palenque, Copan, and elsewhere in the ruins has no more relatedness to the Phoenician than to the Chinese writing. It has not a single characteristic that can be called Phoenician any more than the language of the inscriptions or the style of architecture with which it is associated; therefore we can not reasonably suppose this American civilization originated by people of the Phoenician race, whatever may be thought relative to the supposed ancient communication between the two continents and its probable influence on civilized communities already existing here."(x)
Jason Colavito published articles written by Thomas Crawford Johnston in 1892(z). that he later developed into his 1913 book Did the Phoenicians Discover America [1902+] which is available online(aa).
In his 2009 book, Uncovering Archaeology[1612], Dennis Cassinelli outlines in some detail his Atlantis theory, which he locates in Central America(s). He suggests that the Phoenicians landed in Central America and on seeing the Mayan cities concluded that they had landed in Atlantis. Not unexpectedly, Jason Colavito had a few words to say about this idea(t).
Jean Mazel favourably discussed the idea of the Phoenicians in South America in his 1968 book Avec le Phéniciens.
Hugh Fox (1932-2011) wrote of the early peoples of the Americas in his well-received Gods of the Cataclysm[1171]. The 'cataclysm' referred to is the biblical Deluge, in respect of which he follows the ideas of Velikovsky and the Christian catastrophist Donald W. Patten (1929-2014), who attributed Noah's Flood to a close encounter with a massive extraterrestrial body around 2800 BC. Fox explicitly claims that before the Flood, transoceanic travel was commonplace, with the Chinese in America, Indian theology in the Mediterranean and that after the Flood we had the Phoenicians and Odysseus in America.
More recently, Andrew Collins has drawn attention to the extraction of purple dye from shellfish in a number of Central and South American countries [072.357], commenting that "quite clearly, the presence of purple dye processes in Costa Rica, Mexico and Ecuador, as well as in Peru, could well constitute positive proof of transoceanic contact with ancient seafarers from the eastern Mediterranean." Collins also quotes Thomas Crawford Johnston's Did the Phoenicians Discover America [1902+] – "There is probably no stronger evidence of the presence of the Phoenician in the New World than can be drawn from the use of dyes." Collins also notes that similar sentiments were expressed by Wolfgang Born in a 1937 paper.
Collins has also noted [p361] how cotton was widely produced in the Americas before the Spanish conquest and that genetic studies have shown "that the variety of cotton cultivated in the New World from very earliest times is a hybrid form derived from an Old World species crossed with species native only to the American continent. Crawford Johnston also claimed that the compass had been invented by the Phoenicians!
Peter de Roo, writing in 1900 [890.1.195] stated "that the Phoenicians at some time landed on American soil could not well be denied in the presence of ancient reports; but as Gravier justly observes. if any vague account of their discoveries was kept, it reached us disfigured by Hellenic fanciful imagination."
The late Sabatino Moscati, a renowned linguist and archaeologist, wrote a highly regarded work[505] on the subject of the Phoenicians. Additionally, there is an invaluable website(a) on offer from Salim George Khalaf, a modern Phoenician from Lebanon. This huge site with its 2,000 pages covers all aspects of Phoenician culture. This same site(b), drawing on the work of Ignatius Donnelly, identifies the kings of Atlantis with the Phoenician pantheon and claims that the gods of the Greeks were also the deified Atlantean kings.
Jacques Hébert, who places Atlantis in the Indian Ocean on the island of Socotra, suggests that the Atlanteans had a colony in the Eastern Mediterranean whose inhabitants developed into the Phoenicians!
[1902]+ Did the Phoenicians discover America? : Johnston, Thomas Crawford : Free Download, Borrow, and Streaming : Internet Archive
(a) https://phoenicia.org/index.shtml
(b) https://phoenicia.org/atlantisplato.html
(c) See: https://web.archive.org/web/20101112211128/https://www.forgottenagesresearch.com/vanished-civilizations-series/The-Atlantis-Genesis-ConnectionParallel-Memories-o.htm
(e) https://www.phoenician.org/minoans_phoenicians_paper.htm
(f) https://en.wikipedia.org/wiki/Theory_of_Phoenician_discovery_of_the_Americas
(g) See: Archive 2852
(h) https://www.mysteriousaustralia.com/pyramid-sequel/chapter16.html
(i) https://pt.wikipedia.org/wiki/Bernardo_de_Azevedo_da_Silva_Ramos (Portuguese)
(j) https://archive.org/details/atlastoryoflosti00smitrich
(k) https://www.pappaspost.com/fascinating-cambridge-university-research-links-endangered-pontic-greek-dialect-to-ancient-greek/
(l) https://www.pappaspost.com/fascinating-cambridge-university-research-links-endangered-pontic-greek-dialect-to-ancient-greek/
(m) https://dancingfromgenesis.com/?p=49635
(n) https://phoenicia.org/brazil.html
(o) https://aeon.co/essays/phoenicia-an-imaginary-friend-to-nations-in-need-of-ancestors
(p) https://en.wikipedia.org/wiki/Phoenicia
(q) https://phoenicia.org/index.shtml
(r) https://www.quora.com/Are-the-Phoenicians-Arabs
(s) Lost continent of Atlantis rediscovered | Mesquite Local News (archive.org)
(t) https://www.jasoncolavito.com/blog/nevada-author-claims-phoenicians-mistook-maya-cities-for-atlantis
(u) https://lyme-online.co.uk/news/phoenician-ship-completes-atlantic-voyage/
(v) Wayback Machine (archive.org)
(w) https://web.archive.org/web/20190410091609/https://www.keithhunt.com/Isra5.html
(x) https://www.djmcadam.com/phoenician-theory.html
(y) Phoenicia: an imaginary friend to nations in need of ancestors | Aeon Essays
(z) Phoenicians in America – JASON COLAVITO
(aa) Did the Phoenicians discover America? : Johnston, Thomas Crawford : Free Download, Borrow, and Streaming : Internet Archive
(ab) The Phoenicians | Atlantis in the Java Sea (atlantisjavasea.com)
(ac) Greek Writing System Spans Millennia and Did Not Originate From the Phoenicians – HistoryDisclosure.com (archive.org) *
(ad) https://en.wikipedia.org/wiki/Dispilio_Tablet
(ae) https://en.wikipedia.org/wiki/Archaeological_interest_of_Pedra_da_Gávea
(af) Otto Muck on Phoenicians in America – Atlantisforschung.de (atlantisforschung-de.translate.goog)
(ag) Paul Gaffarel – Atlantisforschung.de (atlantisforschung-de.translate.goog)
(ah) Paul Gallez – Atlantisforschung.de (atlantisforschung-de.translate.goog)
Tagged 2005 Atlantis Conference, America, Andrew Collins, Ann Eliza Smith, Bernardo Silva Ramos, Brazil, Bronze Age, Byblos, Canaanites, Carthaginians, catastrophism, Chinese, Christian C. Karam, Costa Rica, Cotton, Dhani Irwanto, Donald W. Patten, Enrique Onffroy de Thoron, Flood of Noah, Georg Hornius, Henry O'Brien, Herodotus, Hugh Fox, Ignatius Donnelly, Immanuel Velikovsky, Indian Ocean, Indonesia, Ioanna Sitaridou, Ireland, Jacques de Mahieu, Jacques Hebert, James Nienhuis, Jason Colavito, Jean Mazel, Joaquin Lorenz Villanueva, John Denison Baldwin, Jonas Bergman, Josephine Quinn, Lebanon, Libya, Mexico, Odysseus, Otto Muck, Paul Gaffarel, Paul Gallez, Peru, Peter Dawkins, Peter de Roo, Phoenicians, Rex Gilroy, Robert Prutz, Robertus Comtaeus Nortmannus, Roderic O'Flaherty, Romeyka, Sabatino Moscati, Salim George Khalaf, Sidon, Strabo, Thomas Crawford Johnston, Tyre, Tyrhennia, Wolfgang Born


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I'm trying to find a way to create a link that will zip a folder for download.
I've been working with this, but it doesn't seem to work.
I haven't messed with it, but maybe this page would be helpful: http://www.php.net/zip.
If you scroll down, it lists some functions and users have contributed some sample code in the comments section.