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A Bayesian-based method of unconstrained handwritten offline Chinese text line recognition

  • Nan-Xi Li
  • Lian-Wen JinEmail author
Original Paper

Abstract

This paper presents a new Bayesian-based method of unconstrained handwritten offline Chinese text line recognition. In this method, a sample of a real character or non-character in realistic handwritten text lines is jointly recognized by a traditional isolated character recognizer and a character verifier, which requires just a moderate number of handwritten text lines for training. To improve its ability to distinguish between real characters and non-characters, the isolated character recognizer is negatively trained using a linear discriminant analysis (LDA)-based strategy, which employs the outputs of a traditional MQDF classifier and the LDA transform to re-compute the posterior probability of isolated character recognition. In tests with 383 text lines in HIT-MW database, the proposed method achieved the character-level recognition rates of 71.37% without any language model, and 80.15% with a bi-gram language model, respectively. These promising results have shown the effectiveness of the proposed method for unconstrained handwritten offline Chinese text line recognition.

Keywords

Handwritten character recognition Text line recognition Verification Negative training Linear discriminant analysis 

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.College of Educational Information TechnologySouth China Normal UniversityGuangzhouPeople’s Republic of China
  2. 2.School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China

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