CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models
Wu, Yi-Chao1; Yin, Fei1; Liu, Cheng-Lin1,2,3
Source PublicationPATTERN RECOGNITION
2017-05-01
Volume2017Issue:65Pages:251-264
SubtypeArticle
AbstractHandwritten Chinese text recognition based on over-segmentation and path search integrating multiple contexts has been demonstrated successful, wherein the language model (LM) and character shape models play important roles. Although back-off N-gram LMs (BLMs) have been used dominantly for decades, they suffer from the data sparseness problem, especially for high-order LMs. Recently, neural network LMs (NNLMs) have been applied to handwriting recognition with superiority to BLMs. With the aim of improving Chinese handwriting recognition, this paper evaluates the effects of two types of character-level NNLMs, namely, feedforward neural network LMs (FNNLMs) and recurrent neural network LMs (RNNLMs). Both FNNLMs and RNNLMs are also combined with BLMs to construct hybrid LMs. For fair comparison with BLMs and a state-of-the-art system, we evaluate in a system with the same character over-segmentation and classification techniques as before, and compare various LMs using a small text corpus used before. Experimental results on the Chinese handwriting database CASIA-HWDB validate that NNLMs improve the recognition performance, and hybrid RNNLMs outperform the other LMs. To report a new benchmark, we also evaluate selected LMs on a large corpus, and replace the baseline character classifier, over-segmentation, and geometric context models with convolutional neural network (CNN) based models. The performance on both the CASIA-HWDB and the ICDAR-2013 competition dataset are improved significantly. On the CASIA-HWDB test set, the character-level accurate rate (AR) and correct rate (CR) achieve 95.88% and 95.95%, respectively.
KeywordHandwritten Chinese Text Recognition Feedforward Neural Network Language Model Recurrent Neural Network Language Model Hybrid Language Model Convolutional Neural Network Shape Models
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.patcog.2016.12.026
WOS KeywordCHARACTER-RECOGNITION ; DOCUMENT RECOGNITION ; SEGMENTATION ; STRINGS ; ONLINE
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China (NSFC)(61305005 ; 61273269 ; 61573355 ; 61411136002)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000394197700021
Citation statistics
Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13428
Collection模式识别国家重点实验室_模式分析与学习
Affiliation1.Chinese Acad Sci, Inst Inst Automat, NLPR, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Wu, Yi-Chao,Yin, Fei,Liu, Cheng-Lin. Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models[J]. PATTERN RECOGNITION,2017,2017(65):251-264.
APA Wu, Yi-Chao,Yin, Fei,&Liu, Cheng-Lin.(2017).Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models.PATTERN RECOGNITION,2017(65),251-264.
MLA Wu, Yi-Chao,et al."Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models".PATTERN RECOGNITION 2017.65(2017):251-264.
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