Improved Hierarchical Models for Non-Native Chinese Handwriting Recognition Using Hidden Conditional Random Fields

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.