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Proceedings Paper

Recognizing Chinese characters in digital ink from non-native language writers using hierarchical models
Author(s): Hao Bai; Xi-wen Zhang
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Paper Abstract

While Chinese is learned as a second language, its characters are taught step by step from their strokes to components, radicals to components, and their complex relations. Chinese Characters in digital ink from non-native language writers are deformed seriously, thus the global recognition approaches are poorer. So a progressive approach from bottom to top is presented based on hierarchical models. Hierarchical information includes strokes and hierarchical components. Each Chinese character is modeled as a hierarchical tree. Strokes in one Chinese characters in digital ink are classified with Hidden Markov Models and concatenated to the stroke symbol sequence. And then the structure of components in one ink character is extracted. According to the extraction result and the stroke symbol sequence, candidate characters are traversed and scored. Finally, the recognition candidate results are listed by descending. The method of this paper is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.

Paper Details

Date Published: 19 June 2017
PDF: 6 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104430A (19 June 2017); doi: 10.1117/12.2280237
Show Author Affiliations
Hao Bai, Beijing Language and Culture Univ. (China)
Xi-wen Zhang, Beijing Language and Culture Univ. (China)


Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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