Reliable Recognition of Handwritten Digits Using Hamming Network

This paper presents VLSI implementation of handwritten digit recognition system based on analog neural network. The recognition system is based on the least hamming distance neural network which both learning and classification. The circuit is simulated using SPICE tool at 180nm CMOS technology. The proposed circuit is modified version of existing circuit where initially first winner output voltage is set to logic one and rest all to logic zero and also determines next subsequent winner having minimum hamming distance. This type of circuit can be utilized by visual tracking system providing them ability to have backup recognition utility in case first recognized pattern proves to be incorrect. This design shows low power consumption of 34mW.