An oscillatory elastic graph matching model for recognition of offline handwritten Chinese characters

Abstract

Proposes a new application of evolutionary computing - the neural oscillatory elastic graph matching model (NOEGM) for the recognition of offline handwritten Chinese characters. NOEGM consists of three main modules, namely: (1) a feature extraction module using a Gabor filter; (2) a character segmentation module using a neural oscillatory model; and (3) a character recognition module using an elastic graph dynamic link model (EGDLM). In order to optimize the network's performance, a genetic algorithm optimization scheme is integrated into the proposed model. In our research, we applied a sample set of 3,000 handwritten Chinese characters and a test set of 1,000 scanned handwritten Chinese documents to a series of invariant tests, including translation, rotation, dilation and distortion. Experimental results reveal that the overall performance of NOEGM has achieved an average correct recognition rate of over 90%.