Answers.Body: <p>You have explained a pipeline of AI algorithms for text in images: 1) Text detection, 2) OCR, 3) named entity recognition (NER). There are reams of paper on these topics.</p>
  <p><strong>Extracting City and Country Name from Text</strong></p>
  <ul>
  <li><a href="https://iwpnd.pw/articles/2020-02/flashgeotext-library" rel="nofollow noreferrer">Introducing flashgeotext: extract city and country names from text</a></li>
  <li><a href="https://pypi.org/project/locationtagger/" rel="nofollow noreferrer">locationtagger -A Python Package for detecting location</a></li>
  <li><a href="https://towardsdatascience.com/a-comparison-between-spacy-ner-stanford-ner-using-all-us-city-names-c4b6a547290" rel="nofollow noreferrer">A Comparison Between Spacy NER &amp; Stanford NER Using All US City Names</a></li>
  <li><a href="https://stanfordnlp.github.io/CoreNLP/ner.html" rel="nofollow noreferrer">CoreNLP NER</a></li>
  <li><a href="https://ricedh.github.io/03-ner.html" rel="nofollow noreferrer">Detecting Locations with NER</a></li>
  <li><a href="https://paperswithcode.com/task/named-entity-recognition-ner" rel="nofollow noreferrer">Papers with code for NER</a></li>
  <li><a href="https://www.kdnuggets.com/2018/08/named-entity-recognition-practitioners-guide-nlp-4.html" rel="nofollow noreferrer">Named Entity Recognition: A Practitioner’s Guide to NLP</a></li>
  <li><a href="https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da" rel="nofollow noreferrer">Named Entity Recognition with NLTK and SpaCy</a></li>
  </ul>
  <p><strong>Papers on Text from Images</strong></p>
  <ul>
  <li><a href="https://www.mdpi.com/1424-8220/20/20/5946" rel="nofollow noreferrer">A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection</a></li>
  <li><a href="https://arxiv.org/abs/2011.09781" rel="nofollow noreferrer">Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering</a></li>
  <li><a href="https://cybersecurity.springeropen.com/articles/10.1186/s42400-021-00073-x" rel="nofollow noreferrer">An end-to-end text spotter with text relation networks</a></li>
  <li><a href="https://ieeexplore-ieee-org.proxy1.ncu.edu/document/8961031?arnumber=8961031" rel="nofollow noreferrer">Recognizing Text with a CNN</a></li>
  <li><a href="https://ieeexplore.ieee.org/document/9428993" rel="nofollow noreferrer">A Framework to Detect Digital Text Using Android Based Smartphone</a></li>
  <li><a href="https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-ipr.2018.5277" rel="nofollow noreferrer">Detected text‐based image retrieval approach for textual images</a></li>
  <li><a href="https://ieeexplore-ieee-org.proxy1.ncu.edu/document/8563217?arnumber=8563217" rel="nofollow noreferrer">A CNN-Based Approach to Detecting Text from Images of Whiteboards and Handwritten Notes</a></li>
  </ul>
  <p><strong>Websites on Text from Images</strong></p>
  <ul>
  <li><a href="https://nanonets.com/blog/deep-learning-ocr/" rel="nofollow noreferrer">Deep Learning Based OCR for Text in the Wild</a></li>
  <li><a href="https://www.mathworks.com/help/vision/ug/automatically-detect-and-recognize-text-in-natural-images.html" rel="nofollow noreferrer">Automatically Detect and Recognize Text in Natural Images</a></li>
  <li><a href="https://www.pyimagesearch.com/2020/05/25/tesseract-ocr-text-localization-and-detection/" rel="nofollow noreferrer">Tesseract OCR: Text localization and detection</a></li>
  <li><a href="https://www.pyimagesearch.com/2018/08/20/opencv-text-detection-east-text-detector/" rel="nofollow noreferrer">OpenCV Text Detection (EAST text detector)</a></li>
  <li><a href="https://cloud.google.com/vision/docs/ocr" rel="nofollow noreferrer">Detect text in images</a></li>
  <li><a href="https://medium.com/capital-one-tech/learning-to-read-computer-vision-methods-for-extracting-text-from-images-2ffcdae11594" rel="nofollow noreferrer">Learning to Read: Computer Vision Methods for Extracting Text from Images</a></li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-06-03T12:55:15.570Z
Answers.Text: I think the keyword for OP is "pipeline". It is common practice to combine detectors, classifiers and other processing to create a pipeline.
Answers.UserDisplayName: Neil Slater
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-06-03T10:57:39.583Z
Answers.LastActivityDate: 2021-06-03T11:06:25.327Z
Answers.LastEditDate: 2021-06-03T11:06:25.327Z
Answers.UserDisplayName: Brian O'Donnell
Body: <p>I have been working to design a system that uses multiple machine learning models to make sense of data that is dynamically webscraped. Each AI would handle a specific task, for example:</p>
  <p>An AI model would identify text in an image, then attempt to create plain text of what it might be. Once the text is extracted, it would be passed in a stored variable to an AI that can read the text to determine if it is a US city/state.</p>
  <p>I tried to look into if others have done this, but didn't find much on it relating to what I was looking for. Does anyone know if there are potential issues with this? Logically, it looks good to me, but I figured I'd ask.</p>
  <p>If anyone can put me in the right direction for reading material or further information, I would appreciate it.</p>
  
Tags: <natural-language-processing><reference-request><optical-character-recognition><named-entity-recognition><text-detection>
Title: Which AI techniques are there that combine multiple models to make sense of data at different stages?
UserDisplayName: Bipbupbop
ThreadText: # Which AI techniques are there that combine multiple models to make sense of data at different stages?
  
  (Asked by: username_0 on 2021-06-03)
  
  I have been working to design a system that uses multiple machine learning models to make sense of data that is dynamically webscraped. Each AI would handle a specific task, for example:
  
  An AI model would identify text in an image, then attempt to create plain text of what it might be. Once the text is extracted, it would be passed in a stored variable to an AI that can read the text to determine if it is a US city/state.
  
  I tried to look into if others have done this, but didn't find much on it relating to what I was looking for. Does anyone know if there are potential issues with this? Logically, it looks good to me, but I figured I'd ask.
  
  If anyone can put me in the right direction for reading material or further information, I would appreciate it.
  
  **Tags:** natural-language-processing, reference-request, optical-character-recognition, named-entity-recognition, text-detection
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-06-03. Score: 2 | Accepted answer
  
  You have explained a pipeline of AI algorithms for text in images: 1) Text detection, 2) OCR, 3) named entity recognition (NER). There are reams of paper on these topics.
  
  **Extracting City and Country Name from Text**
  
  * [Introducing flashgeotext: extract city and country names from text](https://iwpnd.pw/articles/2020-02/flashgeotext-library)
  * [locationtagger -A Python Package for detecting location](https://pypi.org/project/locationtagger/)
  * [A Comparison Between Spacy NER & Stanford NER Using All US City Names](https://towardsdatascience.com/a-comparison-between-spacy-ner-stanford-ner-using-all-us-city-names-c4b6a547290)
  * [CoreNLP NER](https://stanfordnlp.github.io/CoreNLP/ner.html)
  * [Detecting Locations with NER](https://ricedh.github.io/03-ner.html)
  * [Papers with code for NER](https://paperswithcode.com/task/named-entity-recognition-ner)
  * [Named Entity Recognition: A Practitioner’s Guide to NLP](https://www.kdnuggets.com/2018/08/named-entity-recognition-practitioners-guide-nlp-4.html)
  * [Named Entity Recognition with NLTK and SpaCy](https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da)
  
  **Papers on Text from Images**
  
  * [A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection](https://www.mdpi.com/1424-8220/20/20/5946)
  * [Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering](https://arxiv.org/abs/2011.09781)
  * [An end-to-end text spotter with text relation networks](https://cybersecurity.springeropen.com/articles/10.1186/s42400-021-00073-x)
  * [Recognizing Text with a CNN](https://ieeexplore-ieee-org.proxy1.ncu.edu/document/8961031?arnumber=8961031)
  * [A Framework to Detect Digital Text Using Android Based Smartphone](https://ieeexplore.ieee.org/document/9428993)
  * [Detected text‐based image retrieval approach for textual images](https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-ipr.2018.5277)
  * [A CNN-Based Approach to Detecting Text from Images of Whiteboards and Handwritten Notes](https://ieeexplore-ieee-org.proxy1.ncu.edu/document/8563217?arnumber=8563217)
  
  **Websites on Text from Images**
  
  * [Deep Learning Based OCR for Text in the Wild](https://nanonets.com/blog/deep-learning-ocr/)
  * [Automatically Detect and Recognize Text in Natural Images](https://www.mathworks.com/help/vision/ug/automatically-detect-and-recognize-text-in-natural-images.html)
  * [Tesseract OCR: Text localization and detection](https://www.pyimagesearch.com/2020/05/25/tesseract-ocr-text-localization-and-detection/)
  * [OpenCV Text Detection (EAST text detector)](https://www.pyimagesearch.com/2018/08/20/opencv-text-detection-east-text-detector/)
  * [Detect text in images](https://cloud.google.com/vision/docs/ocr)
  * [Learning to Read: Computer Vision Methods for Extracting Text from Images](https://medium.com/capital-one-tech/learning-to-read-computer-vision-methods-for-extracting-text-from-images-2ffcdae11594)
  
  > **Comments:**
  > **username_2**: I think the keyword for OP is "pipeline". It is common practice to combine detectors, classifiers and other processing to create a pipeline.  
  
  ---
  
  