Knowledge Commons of Institute of Automation,CAS
Unsupervised language model adaptation for handwritten Chinese text recognition | |
Wang, Qiu-Feng; Yin, Fei![]() ![]() | |
发表期刊 | PATTERN RECOGNITION
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2014-03-01 | |
卷号 | 47期号:3页码:1202-1216 |
文章类型 | Article |
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. |
关键词 | Character String Recognition Chinese Handwriting Recognition Unsupervised Language Model Adaptation Language Model Compression |
WOS标题词 | Science & Technology ; Technology |
关键词[WOS] | CHARACTER-RECOGNITION ; OFFLINE RECOGNITION |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000329888800026 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/3088 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
作者单位 | Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
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. |
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