Context Driven Chinese String Segmentation and Recognition
Identifieur interne : 001094 ( Main/Curation ); 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
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Zheng Ren<affiliation><wicri:noCountry code="no comma">E-mail: zheng.ren@siemens.com</wicri:noCountry>
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<front><div type="abstract" xml:lang="en">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.</div>
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