Tensorflow model for OCR
I am new in Tensorflow and I am trying to build model which will be able to perform OCR on my images. I have to read 9 characters (fixed in all images), numbers and letters. My model would be similar to this
https://matthewearl.github.io/2016/05/06/cnn-anpr/
My questions would be, should I train my model against each character firstly and after combine characters to get full label represented. Or I should train on full label straight ?
I know that I need to pass to model, images + labels for corresponding image, what is the format of those labels, is it textual file, I am bit confused about that part, so any explanation about format of labels which are passed to model would be helpful ? I appreciate, thanks.
Answer
There are a couple of ways to deal with this (the following list is not exhaustive).
1) The first one is word classification directly from your image. If your vocabulary of 9 characters is limited you can train a word specific classifier. You can then convolve this classifier with your image and select the word with the highest probability.
2) The second option is to train a character classifier, find all characters in your image, and find the most likely line that has the 9 character you are looking for.
3) The third option is to train a text detector, find all possible text boxes. Then read all text boxes with a sequence-based model, and select the most likely solution that follows your constraints. A simple sequence-based model is introduced in the following paper: http://ai.stanford.edu/~ang/papers/ICPR12-TextRecognitionConvNeuralNets.pdf. Other sequence-based models could be based on HMMs, Connectionist Temporal Classification, Attention based models, etc.
4) The fourth option are attention-based models that work end-to-end to first find the text and then output the characters one-by-one.
Note that this list is not exhaustive, there can be many different ways to solve this problem. Other options can even use third party solutions like Abbyy or Tesseract to help solve your problem.