Bidari, Ravindra Chandrashekar.

Development of a handwritten digit recognition system for real time applications is a feasible goal today due to the many advances pertinent to VLSI. In this research we address the issue of mapping our neural net classification algorithm to Intel's commercially available general purpose Neural Network Chip, 80170NX (ETANN). Most of the proposed techniques used for character recognition have been validated by our research group using various software and hardware simulation methods. The objective of this thesis was to develop a practical hardware system to perform the final step of classification of handwritten digits in an Optical Character Recognition (OCR) system. Such a hardware implementation would increase the classification speed and also would permit testing in a real life application environment. An efficient mapping scheme was evolved to map the modules of a limited interconnect classification algorithm, CLUMP, to a minimum number of ETANN chips. The hardware modules to interface the ETANN chips to MC68000 education board have been developed and tested. The proposed system is estimated to process the features input in 336 $\mu$s, for our specific implementation, with 12 clock phases and 3 ETANN chips.