Context Driven Chinese String Segmentation and Recognition
Identifieur interne : 001094 ( Main/Exploration ); précédent : 001093; suivant : 001095Context Driven Chinese String Segmentation and Recognition
Auteurs : Yan Jiang [République populaire de Chine] ; Xiaoqing Ding [République populaire de Chine] ; Qiang Fu [République populaire de Chine] ; Zheng Ren [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: This paper presents a context driven segmentation and recognition method for handwritten Chinese characters. We follow a split-merge technique in character segmentation. In this process, a Chinese text line is first pre-segmented into a sequence of radicals, which are then merged according to a cost function combining both recognition confidence and contextual cost. Two strategies are also proposed for implementation: bi-gram based merging and lexicon driven merging. In the former one, we generate a set of merging paths which are then evaluated by Viterbi algorithm. The radicals’ best merging method is given by the path with the highest score. In the latter strategy, a lexicon is preset and compared with the radicals to determine both radicals’ merging and candidate character selection. Experiments show that contextual information plays a crucial role in Chinese character segmentation and could obviously improve the segmentation and recognition results.
Url:
DOI: 10.1007/11815921_13
Affiliations:
- Allemagne, République populaire de Chine
- Bade-Wurtemberg, District de Fribourg-en-Brisgau
- Constance (Allemagne), Pékin
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 003104
- to stream Istex, to step Curation: 002E64
- to stream Istex, to step Checkpoint: 000A67
- to stream Main, to step Merge: 001111
- to stream Main, to step Curation: 001094
Le document en format XML
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We follow a split-merge technique in character segmentation. In this process, a Chinese text line is first pre-segmented into a sequence of radicals, which are then merged according to a cost function combining both recognition confidence and contextual cost. Two strategies are also proposed for implementation: bi-gram based merging and lexicon driven merging. In the former one, we generate a set of merging paths which are then evaluated by Viterbi algorithm. The radicals’ best merging method is given by the path with the highest score. In the latter strategy, a lexicon is preset and compared with the radicals to determine both radicals’ merging and candidate character selection. Experiments show that contextual information plays a crucial role in Chinese character segmentation and could obviously improve the segmentation and recognition results.</div></front></TEI><affiliations><list><country><li>Allemagne</li><li>République populaire de Chine</li></country><region><li>Bade-Wurtemberg</li><li>District de Fribourg-en-Brisgau</li></region><settlement><li>Constance (Allemagne)</li><li>Pékin</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Jiang, Yan" sort="Jiang, Yan" uniqKey="Jiang Y" first="Yan" last="Jiang">Yan Jiang</name></noRegion><name sortKey="Ding, Xiaoqing" sort="Ding, Xiaoqing" uniqKey="Ding X" first="Xiaoqing" last="Ding">Xiaoqing Ding</name><name sortKey="Ding, Xiaoqing" sort="Ding, Xiaoqing" uniqKey="Ding X" first="Xiaoqing" last="Ding">Xiaoqing Ding</name><name sortKey="Fu, Qiang" sort="Fu, Qiang" uniqKey="Fu Q" first="Qiang" last="Fu">Qiang Fu</name><name sortKey="Fu, Qiang" sort="Fu, Qiang" uniqKey="Fu Q" first="Qiang" last="Fu">Qiang Fu</name><name sortKey="Jiang, Yan" sort="Jiang, Yan" uniqKey="Jiang Y" first="Yan" last="Jiang">Yan Jiang</name></country><country name="Allemagne"><region name="Bade-Wurtemberg"><name sortKey="Ren, Zheng" sort="Ren, Zheng" uniqKey="Ren Z" first="Zheng" last="Ren">Zheng Ren</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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