OCR and Neuron Network?

Question

I am trying to code an OCR for shop tickets (in Java), I have good results with image dictionary distance, but not for skewed texts or bad scans. I heard that neuronal networks are perfect for this.

Question: which type of neuronal network do you recommand for shop tickets character detection ?

Thks

Answer 1

Neural networks will not magically solve the problem for you. They will have similar problems that your current approach has. Most likely you will have to detect skew and correct it before sending it to a classifier.

Similarly with bad scans. It depends what exactly a bad scan is. For example, some neural networks are amazingly efficient at correcting blurs (unfocused image, blur by move, ...).

Have a look at some papers about OCR and neural networks. It is a classical topic so there are many. For example The Anatomy of Bangla OCR System for Printed Texts Using Back Propagation Neural Network also tries to solve the problem of skewed images before running a neural network.

I know that recurrent neural networks can be used for OCR. Even a very simple one will easily recognize simple characters. There is a recent paper that improves upon them: High-Performance OCR forPrinted English and Fraktur using LSTM Networks. They even include text-line normalization which may be very useful in your case.

Notice that there is an answer here about training a normal Feed-forward backpropagation neural network for OCR too: training feedforward neural network for OCR

Answer 2

"Convolutional Neural Networks" with "Deep Learning" have been shown to give some of the best results in OCR (specifically on the MNIST database).

A good starting point is this tutorial.