Rotation-Free Online Handwritten Chinese Character Recognition using Two-Stage Convolutional Neural Network
Zhe Li, Lianwen Jin, Songxuan Lai · 2018
Handwritten Chinese character recognition (HCCR) is a major research area in pattern recognition. Although recent years have seen tremendous improvement in the use of deep neural networks in handwriting recognition, unconstrained handwriting recognition remains an open problem. Considering the problem of rotated handwritten Chinese character recognition, we propose a two-stage convolutional neural network combined with path signature features to achieve high-accuracy rotation-free HCCR. For handwritten Chinese characters whose rotation angle is up to ±45°, the recognition rate can reach 97.38% on the ICDAR-2013 online HCCR competition dataset, which is comparable to the accuracy achieved by several state-of-the-art methods for non-rotated characters, thus showing the effectiveness of the proposed method.