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Deep convolutional neural networks (DCNNs) have achieved great success invarious computer vision and pattern recognition applications, including thosefor handwritten Chinese character recognition (HCCR). However, most currentDCNN-based HCCR approaches treat the handwritten sample simply as an imagebitmap, ignoring some vital domain-specific information that may be useful butthat cannot be learnt by traditional networks. In this paper, we propose anenhancement of the DCNN approach to online HCCR by incorporating a variety ofdomain-specific knowledge, including deformation, non-linear normalization,imaginary strokes, path signature, and 8-directional features. Our contributionis twofold. First, these domain-specific technologies are investigated andintegrated with a DCNN to form a composite network to achieve improvedperformance. Second, the resulting DCNNs with diversity in their domainknowledge are combined using a hybrid serial-parallel (HSP) strategy.Consequently, we achieve a promising accuracy of 97.20% and 96.87% onCASIA-OLHWDB1.0 and CASIA-OLHWDB1.1, respectively, outperforming the bestresults previously reported in the literature.

Improved Deep Convolutional Neural Network For Online Handwritten Chinese Character Recognition using Domain-Specific Knowledge