Deep convolutional neural networks (DCNNs) have achieved great success in various computer vision and pattern recognition applications, including those for handwritten Chinese character recognition (HCCR). However, most current DCNN based HCCR approaches treat the handwritten sample simply as an image bitmap, ignoring some vital domain-specific information that may be useful but that cannot be learnt by traditional networks. In this paper, we propose an enhancement of the DCNN approach to online HCCR by incorporating a variety of domain specific knowledge, including deformation, non-linear normalization, imaginary strokes, path signature, and 8directional features. Our contribution is twofold. First, these domain-specific technologies are investigated and integrated with a DCNN to form a composite network to achieve improved performance. Second, the resulting DCNNs with diversity in their domain knowledge are combined using a hybrid serial-parallel (HSP) strategy. Consequently, we achieve a promising accuracy of 97.20% and 96.87% on CASIA-OLHWDBl.O and CASIA OLHWDBl.l, respectively, outperforming the best results previously reported in the literature.
Yang et.al REF propose to use various domain-specific knowledge i.e, deformation transformation, non-linear normalization,imaginary stroke, path signature features, eight-directional features, to improve DCNN based OLHCCR.
Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge
2015 13th International Conference on Document Analysis and Recognition (ICDAR)
2015 13th International Conference on Document Analysis and Recognition (ICDAR)
Computer Science