Alexander Shustorovich, Christopher W. Thrasher
This paper describes the Kodak Imageliok TM OCR alphanumeric handprint module. There are two neural network algorithms at its cme: the first network is trained to find individual characters in an alphamuneric field, while the second one perfmns the classification. Both networks were trained on Gabor projections of the ociginal pixel images, which resulted in higher recognition rates and greater noise immunity. Compared its purely numeric counterpart (Shusurovich and Thrasher, 1995), this version of the system has a significant applicatim specific postprocessing module. The system has been implemented in specialized parallel hardware, which allows it to run at 80 char/sec/board. It has been installed at the Driver and Vehicle Licensing Agency (DVLA) in the United Kingdom. and its overall success rate exceeds 96% (character level without rejects). which translates into 85% field rate. If approximately 20% of the fields are rejected. the system achieves 99.8% character and 99.5% field success rate.