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In the previous chapter you learned how to construct a Kohonen neural network. You learned that a Kohonen neural network can be used to classify samples into several groups. In this chapter we will closely examine a specific application of the Kohonen neural network. The Kohonen neural network will be applied to Optical Character Recognition (OCR).

OCR programs are capable of reading printed text. This could be text that was scanned in from a document, or hand written text that was drawn to a hand-held device, such as a Personal Digital Assistant (PDA). OCR programs are used widely in many industries. One of the largest uses of OCR systems is the United States Postal Service. In the 1980's the US Postal Service had many Letter Sorting Machines (LSMs). These machines were manned by human clerks that would key the zip codes of sixty letters per minute.

These human letter sorters have now been completely replaced by computerized letter sorting machine. This new generation of letter sorting machines is enabled with OCR technology. These machines scan the incoming letters and read the zip code. Using the ZIP code these letters can be routed to their correct destination cities.

This chapter will develop an example program that can be trained to recognize human handwriting. We will not create a program that can scan pages of text. Rather this program will read character by character, as the user draws them. This function will be similar to the handwriting recognition used by many PDA's.