Cascade Markov random fields for stroke extraction of Chinese characters

Jia Zeng, Wei Feng, Lei Xie, Zhi Qiang Liu

科研成果: 期刊稿件文章同行评审

20 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)301-311
页数11
期刊Information Sciences
180
2
DOI
出版状态已出版 - 15 1月 2010

指纹

探究 'Cascade Markov random fields for stroke extraction of Chinese characters' 的科研主题。它们共同构成独一无二的指纹。

引用此