Answers.Body: <p>What you call is a typical OCR application. There are two things to consider :</p>
  
  <p>1- Localizing the text</p>
  
  <p>2- Recognizing the text</p>
  
  <p>Please note that it is not an easy task to solve the problem globally. However, depending on your working subset, you need to get the data first. There are a lot of open source ocr database that you can find. 
  Then, you need to add some preprocessing and it depends on which kind of path you want to choose. You can find lots of articles about it. 
  Then, you need to add a localization network (like Faster R-CNN or any specialized one for OCR), with the combination of recognition network. 
  This was deep learning approach. It has a learning curve if you have never worked with deep learning frameworks.</p>
  
  <p>Another way that you can take is to get different character image dataset. Resize all of them to same size, flatten to 1-D and train a classifier. Then, you can start to distinguish different image patches if they are character or not (or which character). Then, for inference, you need to crop different sub-regions from input image and feed it to classifier to get a result. 
  It is more costly in terms of inference time and the success rate will more likely to be much lower than deep learning method but you can learn &amp; implement this one much quicker. </p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-11-15T01:19:29.117Z
Answers.LastActivityDate: 2017-11-15T01:19:29.117Z
Answers.UserDisplayName: Deniz Beker
Body: <p>I want to capture text and letters on images (png, jpeg, etc.).  Is it Possible Which algorithm/software can I use?</p>
  
  <p>Right now I am using <code>R</code> with the <code>tesseract</code> package but it's not solving my problem: if letters on images are  different colors and different format, sizes in those situations don't work.</p>
  
  <p>I don't have the any training data to compare text on the image with the existing data set. So in those situations is it possible?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-11-13T12:25:33.217Z
Comments.Text: go to this link [GitHub](https://github.com/schollz/python-ocr) . i think this can help you.
Comments.UserDisplayName: syam
Tags: <deep-learning><artificial-neuron>
Title: Text Capturing on the Images
UserDisplayName: gopalakrishna
ThreadText: # Text Capturing on the Images
  
  (Asked by: username_0 on 2017-11-13)
  
  I want to capture text and letters on images (png, jpeg, etc.). Is it Possible Which algorithm/software can I use?
  
  Right now I am using `R` with the `tesseract` package but it's not solving my problem: if letters on images are different colors and different format, sizes in those situations don't work.
  
  I don't have the any training data to compare text on the image with the existing data set. So in those situations is it possible?
  
  **Tags:** deep-learning, artificial-neuron
  > **Comments:**
  > **username_1**: go to this link [GitHub](https://github.com/schollz/python-ocr) . i think this can help you.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2017-11-15. Score: 0 | Accepted answer
  
  What you call is a typical OCR application. There are two things to consider :
  
  1- Localizing the text
  
  2- Recognizing the text
  
  Please note that it is not an easy task to solve the problem globally. However, depending on your working subset, you need to get the data first. There are a lot of open source ocr database that you can find.
  Then, you need to add some preprocessing and it depends on which kind of path you want to choose. You can find lots of articles about it.
  Then, you need to add a localization network (like Faster R-CNN or any specialized one for OCR), with the combination of recognition network.
  This was deep learning approach. It has a learning curve if you have never worked with deep learning frameworks.
  
  Another way that you can take is to get different character image dataset. Resize all of them to same size, flatten to 1-D and train a classifier. Then, you can start to distinguish different image patches if they are character or not (or which character). Then, for inference, you need to crop different sub-regions from input image and feed it to classifier to get a result.
  It is more costly in terms of inference time and the success rate will more likely to be much lower than deep learning method but you can learn & implement this one much quicker.
  
  
  ---
  
  