Improved hierarchical models for non-native Chinese handwriting recognition using hidden conditional random fields

Hao Bai; Xi-Wen Zhang

doi:10.1117/12.2574420

24 June 2020 Improved hierarchical models for non-native Chinese handwriting recognition using hidden conditional random fields

Hao Bai, Xi-Wen Zhang

Author Affiliations +

Proceedings Volume 11526, Fifth International Workshop on Pattern Recognition; 1152609 (2020) https://doi.org/10.1117/12.2574420
Event: Fifth International Workshop on Pattern Recognition, 2020, Chengdu, China

Abstract

Hierarchical models with HMM has the advantage of recognizing Chinese characters in digital ink from non-native language writers. However, the recognition performance has been limited by the attribute of generative model of HMM. In this paper, we apply Hidden Conditional Random Field to improve the performance of hierarchical models. First, strokes in one Chinese character are classified with HCRF and then concatenated to the stroke symbol sequence. In the meantime, 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 approach proposed is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.

Citation Download Citation

Hao Bai and Xi-Wen Zhang "Improved hierarchical models for non-native Chinese handwriting recognition using hidden conditional random fields", Proc. SPIE 11526, Fifth International Workshop on Pattern Recognition, 1152609 (24 June 2020); https://doi.org/10.1117/12.2574420

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