MODULAR PARTIALLY CONNECTED NEURAL NETWORK VIA HIDDEN LAYER COUPLING FOR OFF-LINE HANDWRITTEN HANGUL RECOGNITION

This paper presents a study on the off-line handwritten Hangul (Korean) character recognition based on modular neural network employing partial connections between the hidden nodes and one global and ten local receptive fields. A modular architecture called modular partial connected neural network (MPCNN) has been proposed here. This MPCNN combines three partially connected neural networks (PCNNs) trained on three different feature sets into a hierarchically organized MLP with their truncated subnetwork as basic building blocks. This subnetwork combination has been achieved via internal hidden layer coupling rather than conventional output combination that attracts considerable attention recently. The performance of the proposed classifier has been verified on the recognition of 18 off-line handwritten Hangul characters widely used in business cards in Korea.