A modified current mode Hamming neural network for totally unconstrained handwritten numeral recognition

A compact smart current mode Hamming neural network for classifying complex patterns such as totally unconstrained handwritten digits is presented. It is based on multi-threshold template matching, multistage matching and k-WTB (k-winner-taker-all). The neural classifier consists of two kinds of templates: one is a binary template and the other is a multi-value programmable template, each of them has its own threshold and realized in MOS current mirrors, the current mode k-WTA which is reconfigurable is put forward. The second stage matching templates are programmable from outside the chip. This mixed analog-digital Hamming neural classifier can be fabricated in a standard digital CMOS technology.