Neural network character recognition example

Character Recognition Example: An Explanation On the Simple Concept Used in This Demo
After I posted these series of examples, I’ve received a few comments from readers. Some of them using different version of software and can’t run the program successfully, some were happy with the simple code in which they had applied it to some other application, while a few readers were asking for more explanations on how these code work.
ell, I believe I’ve answered the doubt on the different version issue, but not the doubt on more details explanations. Here, I tried to draft a few paragraph to explain the concept hidden behind the code, and hope it helps to answer the readers who ask me about it though email.
1. Image Preprocessing
he image is first being converted to grayscale image follow by the threshing technique, which make the image become binary image. The binary image is then go through connectivity test in order to check for the maximum connected component, which is, the box of the form. After locating the box, the individual characters are then cropped into different sub images that are the raw data for the following feature extraction routine.
The size of the sub-images are not fixed since they are expose to noises which will affect the cropping process to be vary from one to

Source: what I know about IMAGE Processing

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