Markov Random Fields for Handwritten Chinese Character Recognition

Authors:
Jia Zeng;Zhi-Qiang Liu
Affiliations:
City University of Hong Kong;City University of Hong Kong
Venue:
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Year:
2005

Citing 3
Cited 2

Markov random field modeling in image analysis

Markov random field modeling in image analysis
A Discrete Contextual Stochastic Model for the Offline Recognition of Handwritten Chinese Characters

IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwriting Recognition

Handwriting Recognition

Building compact recognizer with recognition rate maintained for on-line handwritten Japanese text recognition

Pattern Recognition Letters
Building a compact online MRF recognizer for large character set by structured dictionary representation and vector quantization technique

Pattern Recognition

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Abstract

In this paper, we propose a statistical-structural scheme for Chinese character modeling based on Markov random fields (MRFs). We use 2-D Gabor filters to extract directional stroke segments from images of Chinese characters, where each stroke segment is associated with a state in Markov random field models. The structural information is described by neighborhood system and pair-state clique potentials; meanwhile the statistical information is represented by single-state probability density functions (pdfs). Extensive experiments on similar characters have been carried out on the database ETL9B. The experimental results confirm that Markov random field models are effective in modeling both statistical and structural information of Chinese characters, and works well for handwritten Chinese character recognition.