Cascade Markov random fields for stroke extraction of Chinese characters

  • Authors:

  • Jia Zeng;Wei Feng;Lei Xie;Zhi-Qiang Liu

  • Affiliations:

  • Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;School of Computer Science, Northwestern Polytechnical University, Xi'an, PR China;School of Creative Media, City University of Hong Kong, Tat Chee Avenue 83, Hong Kong

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

Extracting perceptually meaningful strokes plays an essential role in modeling structures of handwritten Chinese characters for accurate character recognition. This paper proposes a cascade Markov random field (MRF) model that combines both bottom-up (BU) and top-down (TD) processes for stroke extraction. In the low-level stroke segmentation process, we use a BU MRF model with smoothness prior to segment the character skeleton into directional substrokes based on self-organization of pixel-based directional features. In the high-level stroke extraction process, the segmented substrokes are sent to a TD MRF-based character model that, in turn, feeds back to guide the merging of corresponding substrokes to produce reliable candidate strokes for character recognition. The merit of the cascade MRF model is due to its ability to encode the local statistical dependencies of neighboring stroke components as well as prior knowledge of Chinese character structures. Encouraging stroke extraction and character recognition results confirm the effectiveness of our method, which integrates both BU/TD vision processing streams within the unified MRF framework.