I worked through the PyTorch version of the first course (Thanks, Jeremy!!!) and am now working on the second course. This is so great!!!
For a practical application of what I 've learned, I want to build a piece of code to read number plates (of European) cars. The number plates are extracted with a Yolo Object Detector from the images of the cars (that works well), and then should be read. The extraction works well.
I have tried to feed the number plates into a classical segmentation algorithm (based on filters from OpenCV, being a conventional feature extractor) to segment the letters, and then fed each single letter into a classifying network (similar to letter classification for MNIST).
The problem is the conventional segmentation algorithm which fails if the plates are dirty or shadowed. Is there an existing solution to read words from images?
I tried Tesseract also but this did not work well, since the plates can contain logos etc, that should not be read.
Can a kind soul recommend a paper or resource how to read small patches of text (license plates, street signs,…) end to end. They are localised, so they do not be read anywhere in the image, only in a very localised part.
I can not follow the classical approach of reading words against a dictionary (like Tesseract does) since there is no dictionary for the license plates :-). I have made searches, but could not come up with a good solution.
Kind regards and thanks a lot,