Handwritten Chinese text recognition by integrating multiple contexts

Qiu Feng Wang*, Fei Yin, Cheng Lin Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

151 Citations (Scopus)

Abstract

This paper presents an effective approach for the offline recognition of unconstrained handwritten Chinese texts. Under the general integrated segmentation-and-recognition framework with character oversegmentation, we investigate three important issues: candidate path evaluation, path search, and parameter estimation. For path evaluation, we combine multiple contexts (character recognition scores, geometric and linguistic contexts) from the Bayesian decision view, and convert the classifier outputs to posterior probabilities via confidence transformation. In path search, we use a refined beam search algorithm to improve the search efficiency and, meanwhile, use a candidate character augmentation strategy to improve the recognition accuracy. The combining weights of the path evaluation function are optimized by supervised learning using a Maximum Character Accuracy criterion. We evaluated the recognition performance on a Chinese handwriting database CASIA-HWDB, which contains nearly four million character samples of 7,356 classes and 5,091 pages of unconstrained handwritten texts. The experimental results show that confidence transformation and combining multiple contexts improve the text line recognition performance significantly. On a test set of 1,015 handwritten pages, the proposed approach achieved character-level accurate rate of 90.75 percent and correct rate of 91.39 percent, which are superior by far to the best results reported in the literature.

Original languageEnglish
Article number6112767
Pages (from-to)1469-1481
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume34
Issue number8
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Handwritten Chinese text recognition
  • candidate character augmentation
  • confidence transformation
  • geometric models
  • language models
  • maximum character accuracy training
  • refined beam search

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