Unsupervised language model adaptation for handwritten Chinese text recognition

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	Unsupervised language model adaptation for handwritten Chinese text recognition
	Wang, Qiu-Feng; Yin, Fei; Liu, Cheng-Lin
刊名	PATTERN RECOGNITION
	2014-03-01
卷号	47 期号:3 页码:1202-1216
关键词	Character string recognition Chinese handwriting recognition Unsupervised language model adaptation Language model compression
英文摘要	This paper presents an effective approach for unsupervised language model adaptation (LMA) using multiple models in offline recognition of unconstrained handwritten Chinese texts. The domain of the document to recognize is variable and usually unknown a priori, so we use a two-pass recognition strategy with a pre-defined multi-domain language model set. We propose three methods to dynamically generate an adaptive language model to match the text output by first-pass recognition: model selection, model combination and model reconstruction. In model selection, we use the language model with minimum perplexity on the first-pass recognized text. By model combination, we learn the combination weights via minimizing the sum of squared error with both L2-norm and L1-norm regularization. For model reconstruction, we use a group of orthogonal bases to reconstruct a language model with the coefficients learned to match the document to recognize. Moreover, we reduce the storage size of multiple language models using two compression methods of split vector quantization (SVQ) and principal component analysis (PCA). Comprehensive experiments on two public Chinese handwriting databases CASIA-HWDB and HIT-MW show that the proposed unsupervised LMA approach improves the recognition performance impressively, particularly for ancient domain documents with the recognition accuracy improved by 7 percent. Meanwhile, the combination of the two compression methods largely reduces the storage size of language models with little loss of recognition accuracy. (C) 2013 Elsevier Ltd. All rights reserved.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
关键词[WOS]	CHARACTER-RECOGNITION ; OFFLINE RECOGNITION
收录类别	SCI
语种	英语
WOS记录号	WOS:000329888800026
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3088]
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	Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Qiu-Feng,Yin, Fei,Liu, Cheng-Lin. Unsupervised language model adaptation for handwritten Chinese text recognition[J]. PATTERN RECOGNITION,2014,47(3):1202-1216.
APA	Wang, Qiu-Feng,Yin, Fei,&Liu, Cheng-Lin.(2014).Unsupervised language model adaptation for handwritten Chinese text recognition.PATTERN RECOGNITION,47(3),1202-1216.
MLA	Wang, Qiu-Feng,et al."Unsupervised language model adaptation for handwritten Chinese text recognition".PATTERN RECOGNITION 47.3(2014):1202-1216.