I trained some CNN model for license plate recognition using stacked LSTM and convolutional layers, but I got stuck in %88 accuracy. (This accuracy is on the whole license plate not one character).
For training my model I used categorical cross entropy. After some search I have found that a good loss function for image OCR is CTC loss, but I don't know exactly it is the right choice or not because of two reasons:
first, each license plate has fixed length of character (8 chars) so they don't have variable length which is the problem CTC loss function solves.
Secondly, the characters are not necessarily correlated like languages or NLP tasks.
Could anyone guide me is CTC right choice or not? if yes why and if no for my task which loss function is suitable for my task.