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This is great work for someone who's been dabbling with deep learning. I used to work on OCR systems for postal address recognition and for the most part this

by tgflynn 10y ago

This is great work for someone who's been dabbling with deep learning. I used to work on OCR systems for postal address recognition and for the most part this is how I would have approached the problem. A couple of suggestions: You will almost certainly get better accuracy with a broader array of input fonts. I'm not sure how well you can simulate all of the image conditions that might occur in a real system so adding some real images to the training set (if you can find them - but I suspect there are OCR type training sets available these days) would probably help. For the speed problem I like the idea of using a simpler net, probably with a course scale image for pre-detection. You can set an appropriate detection threshold to avoid false negatives and thereby avoid running the full net on the vast majority of images which won't contain anything that looks like a plate number.

PeterisP 10y ago
Can you elaborate on why a broader array of input fonts would help? Wouldn't best accuracy come from obtaining the single font that is actually used for the licence plates (or finding the closest match), since in this scenario there isn't any variation as all plates are made according to a single standard?
- tgflynn 10y ago
  Even if all the fonts are rigorously identical the viewing conditions won't be. There will be plates seen at various angles, bent plates, dented plates, dirty plates etc. I think that if you're going to use a net you should try to train it in such a way that it learns to generalize the fundamental shape characteristics of the characters, much like the human visual system does, and that requires training with a wide range of different type of characters, all considered to be of the same class. If you're convinced that you only need to deal with identical characters you're probably better off using some form of template matching. But I think this is rarely the case for real world systems.

by tgflynn 10y ago

PeterisP 10y ago

Can you elaborate on why a broader array of input fonts would help? Wouldn't best accuracy come from obtaining the single font that is actually used for the licence plates (or finding the closest match), since in this scenario there isn't any variation as all plates are made according to a single standard?

tgflynn 10y ago
Even if all the fonts are rigorously identical the viewing conditions won't be. There will be plates seen at various angles, bent plates, dented plates, dirty plates etc. I think that if you're going to use a net you should try to train it in such a way that it learns to generalize the fundamental shape characteristics of the characters, much like the human visual system does, and that requires training with a wide range of different type of characters, all considered to be of the same class. If you're convinced that you only need to deal with identical characters you're probably better off using some form of template matching. But I think this is rarely the case for real world systems.