Chinese to Braille Translation Based on Braille Word Segmentation Using Statistical Model

Automatic translation of Chinese text to Chinese Braille is important for blind people in China to acquire information using computers or smart phones. In this paper, a novel scheme of Chinese-Braille translation is proposed. Under the scheme, a Braille word segmentation model based on statistical m...

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Published inShanghai jiao tong da xue xue bao Vol. 22; no. 1; pp. 82 - 86
Main Author 王向东 杨阳 张金超 姜文斌 刘宏 钱跃良
Format Journal Article
LanguageEnglish
Published Shanghai Shanghai Jiaotong University Press 01.02.2017
Springer Nature B.V
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ISSN1007-1172
1995-8188
DOI10.1007/s12204-017-1804-x

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Summary:Automatic translation of Chinese text to Chinese Braille is important for blind people in China to acquire information using computers or smart phones. In this paper, a novel scheme of Chinese-Braille translation is proposed. Under the scheme, a Braille word segmentation model based on statistical machine learning is trained on a Braille corpus, and Braille word segmentation is carried out using the statistical model directly without the stage of Chinese word segmentation. This method avoids establishing rules concerning syntactic and semantic information and uses statistical model to learn the rules stealthily and automatically. To further improve the performance, an algorithm of fusing the results of Chinese word segmentation and Braille word segmentation is also proposed. Our results show that the proposed method achieves accuracy of 92.81% for Braille word segmentation and considerably outperforms current approaches using the segmentation-merging scheme.
Bibliography:31-1943/U
WANG Xiangdong;YANG Yang;ZHANG Jinchao;JIANG Wenbin;LIU Hong;QIAN Yueliang;Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology,Chinese Academy of Sciences;Institute of Computing Technology,Chinese Academy of Sciences;Jiangsu Enterprise Information Operation Center,China Telecom Corporation Limited
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ISSN:1007-1172
1995-8188
DOI:10.1007/s12204-017-1804-x