A Connection Reduced Network for Similar Handwritten Chinese Character Discrimination

Abstract

One difficulty in handwritten Chinese character recognition (HCCR) is due to the large number of similar characters. In this study, we propose a connection reduced network (CRN) to discriminate similar pairs. Each hidden neuron in CRN is restricted to has one input signal and the strength of this input is set as a variable which is selected from the input of the network. Experimental results based on 100 similar pairs demonstrate that the proposed method yields highly competitive test recognition results compared to the state-of-the-art methods, while consuming less memory and time resources.