Paper Info
Title
Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge
Abstract
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 8-directional 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-OLHWDB1.0 and CASIA-OLHWDB1.1, respectively, outperforming the best results previously reported in the literature.
Year
DOI
Venue
2015
10.1109/ICDAR.2015.7333822
International Conference on Document Analysis and Recognition
Keywords
Field
DocType
Handwritten Chinese character recognition, deep convolutional neural network, domain-specific knowledge, hybrid serial-parallel ensemble strategy
Neocognitron,Normalization (statistics),Pattern recognition,Domain knowledge,Character recognition,Convolutional neural network,Computer science,Artificial intelligence,Bitmap,Machine learning
Journal
Volume
ISSN
Citations 
abs/1505.07675
1520-5363
18
PageRank 
References 
Authors
0.61
19
4
Name
Order
Citations
PageRank
Weixin Yang11059.16
Lianwen Jin21337113.14
zecheng xie3967.55
Ziyong Feng41276.86