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Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models

文献类型：期刊论文

作者Wu, Yi-Chao1; Yin, Fei1; Liu, Cheng-Lin1,2,3
刊名PATTERN RECOGNITION
出版日期2017-05-01
卷号2017期号:65页码:251-264
关键词Handwritten Chinese Text Recognition Feedforward Neural Network Language Model Recurrent Neural Network Language Model Hybrid Language Model Convolutional Neural Network Shape Models
DOI10.1016/j.patcog.2016.12.026
文献子类Article
英文摘要Handwritten 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.
WOS关键词CHARACTER-RECOGNITION ; DOCUMENT RECOGNITION ; SEGMENTATION ; STRINGS ; ONLINE
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000394197700021
资助机构National Natural Science Foundation of China (NSFC)(61305005 ; 61273269 ; 61573355 ; 61411136002)
源URL[http://ir.ia.ac.cn/handle/173211/13428]  
专题自动化研究所_模式识别国家重点实验室_模式分析与学习团队
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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