ored in memory. Most OCR systems are designed specifically for reading forms which are produced for that purpose. Other systems can achieve good results with machine printed text in almost all font styles. However, none of the systems is capable of recognizing handwritten characters. This is because every person writes differently. Nestor, a company based in Providence, Rhode Island has developed handwriting recognition products based on developments in neural network computers. Their system, NestorReader, recognizes handwritten characters by extracting data sets, or feature vectors, from each character. The system processes the input representations using a collection of three by three pixel edge templates (Pennisi, 23). The system then lays a grid over the pixel array and pieces it together to form a letter. Then the network discovers which letter the feature vector most closely matched. The system can learn through trial and error, and it has an accuracy of about 80 percent. Eventually this system will be able to evaluate all symbols with equal accuracy. It is possible to implement new neural-network based OCR systems into standard large optical systems. Those older systems, used for automated processing of forms and documents, are limited to reading typed block letters. When added to these systems, neural networks improve accuracy of reading not only typed letters but also handwritten characters. Along with automated form processing, neural networks will analyze signatures for possible forgeries.Conclusion Neural networks are still considered emerging technology and have a long way to go toward achieving their goals. This is certainly true for financial transaction security. But with the current capabilities, neural networks can certainly assist humans in complex tasks where large amounts of data need to be analyzed. For visual recognition of individual cu...
