CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Handwritten Chinese character recognition by joint classification and similarity ranking
Cheng Cheng1; Xu-Yao Zhang2; Xiao-Hu Shao1; Xiang-Dong Zhou1
2016
Conference NameInternational Conference on Frontiers in Handwriting Recognition (ICFHR)
Source PublicationInternational Conference on Frontiers in Handwriting Recognition (ICFHR)
Conference DateOctober 23-26
Conference PlaceShenzhen, China
Abstract
Deep convolutional neural networks (DCNN) have
recently achieved state-of-the-art performance on handwritten
Chinese character recognition (HCCR). However, most of DCNN
models employ the softmax activation function and minimize
cross-entropy loss, which may loss some inter-class information.
To cope with this problem, we demonstrate a small but consistent
advantage of using both classification and similarity ranking
signals as supervision. Specifically, the presented method learns a
DCNN model by maximizing the inter-class variations and minimizing
the intra-class variations, and simultaneously minimizing
the cross-entropy loss. In addition, we also review some loss
functions for similarity ranking and evaluate their performance.
Our experiments demonstrate that the presented method achieves
state-of-the-art accuracy on the well-known ICDAR 2013 offline
HCCR competition dataset.
KeywordHccr
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12470
Collection模式识别国家重点实验室_模式分析与学习
Corresponding AuthorCheng Cheng
Affiliation1.重庆绿色科学研究院
2.中科院自动化所
Recommended Citation
GB/T 7714
Cheng Cheng,Xu-Yao Zhang,Xiao-Hu Shao,et al. Handwritten Chinese character recognition by joint classification and similarity ranking[C],2016.
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