CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Radical-Based Chinese Character Recognition via Multi-Labeled Learning of Deep Residual Networks
Wang TQ(王铁强)1,2; Yin F(殷飞)1; Liu CL(刘成林)1,2; Cheng-Lin Liu
2017
Conference NameThe 14th IAPR International Conference on Document Analysis and Recognition
Source PublicationProc. 14th Int. Conf. Document Analysis and Recognition
Pages579-584
Conference DateNovember 13-15, 2017
Conference PlaceKyoto, Japan
Project NumberNSFC-61573355 ; NSFC-61633021
Funding OrganizationOsaka Prefecture University, Japan
AbstractThe digitization of Chinese historical documents poses a new challenge that in the huge set of character categories, majority of characters are not in common use now and have few samples for training the character classifiers. To settle this problem, we consider the radical-level composition of Chinese characters, and propose to detect position-dependent radicals using a deep residual network with multi-labeled learning. This enables the recognition of novel characters without training samples if the characters are composed of radicals appearing in training samples. In multi-labeled learning, each training character sample is labeled as positive for each radical it contains, such that after training, all the radicals appearing in the character can be detected. Experimental results on a large-category-set database of printed Chinese characters demonstrate that the proposed method can detect radicals accurately. Moreover, according to radical configurations, our model can credibly recognize novel characters as well as trained characters.
KeywordChinese Character Recognition Radical Detection Deep Residual Network Multi-labeled Learning
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Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19971
Collection模式识别国家重点实验室_模式分析与学习
Corresponding AuthorCheng-Lin Liu
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang TQ,Yin F,Liu CL,et al. Radical-Based Chinese Character Recognition via Multi-Labeled Learning of Deep Residual Networks[C],2017:579-584.
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