Automated error detection and correction of chinese characters in written essays based on weighted finite-state transducer

Shudong Hao, Zongtian Gao, Mingqing Zhang, Yanyan Xu, Hengli Peng, Kaile Su, Dengfeng Ke

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

Chinese text error detection and correction is widely applicable, but the methods so far are not robust enough for industrial use. In this paper, a new method is proposed based on Tri-gram modeled-Weighted Finite-State Transducer (WFST). By integrating confusing-character table, beam search and A* search, we evaluate the performance on real test essays. Various experiments have been conducted to prove that the proposed method is effective with the recall rate of 85.68%, the detection accuracy of 91.22% and the correction accuracy of 87.30%.

Original languageEnglish
Article number6628721
Pages (from-to)763-767
Number of pages5
JournalProceedings of the International Conference on Document Analysis and Recognition, ICDAR
DOIs
StatePublished - 2013
Event12th International Conference on Document Analysis and Recognition, ICDAR 2013 - Washington, DC, United States
Duration: 25 Aug 201328 Aug 2013

Keywords

  • Error correction
  • Error detection
  • N-gram language model
  • Weighted Finite-State Transducer (WFST)

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