Introduction
There are many different approaches to optical character recognition problem. One of the most common and popular approaches is based on neural networks, which can be applied to different tasks, such as pattern recognition, time series prediction, function approximation, clustering, etc.
In this article, I'll try to review some approaches for optical character recognition using artificial neural networks. The attached project is aimed as a research project, so don't try to find here a ready solution for scanned document processing.
Test application
A test application is provided with the article, which tries to implement the second approach. How to use it? Let's try the first test:
* We need to generate initial receptors set. On application startup it's already generated, so we can skip this step, if are not planning to change the initial amount of receptors or the filtered amount.
* Select fonts, which will be used for teaching network. Let it be the regular Arial font for the first time.
* Generate data. In this step the initial training data will be generated.
* Filter data. In this step the initial receptors set as well as the training data will be filtered.
* Create network - a neural network will be created.
* Train network - neural networks training.
* Let's look at the misclassified value. It should be "0/26", which means that the trained network can successfully recognize all patterns from the training set.
* We can ensure this by using the "Draw" and "Recognize" buttons.
After performing all these steps we find that the concept is working! You can try a more complex test, choosing all regular fonts. But, don't forget to turn on the "Scale" option before data generation. The option will scale all images from the training set. Then you can set the error limit of the second pass to "0.5" for faster training or leave it "0.1" if you are not in a hurry. At the end of training you should get a misclassified value of "0 / 130". You can check that all images from the training set can be recognized.
You can even try to teach a network all fonts: regular and italic. You should use "Scale" option for it and you will need to play a little bit with the learning speed and error limit values. You can also try to use two-layered network. I was able to get a misclassified value of "4/260" with only 100 receptors.
老虎 2009年02月26日[编辑]