• DocumentCode
    2497779
  • Title

    An improved method on Chinese character recognition

  • Author

    Wang, Ke-jian ; Liu, Dong-rong ; Zhao, Yan ; Han, Xian-zhong

  • Author_Institution
    Sch. of Inf. Sci. & Technol., Agric. Univ. Hebei, China
  • Volume
    5
  • fYear
    2003
  • fDate
    2-5 Nov. 2003
  • Firstpage
    3072
  • Abstract
    Genetic algorithm (GA) is an effective method to optimize system parameters. Chinese character recognition post-processing (CCRP) based on GA gets good result, but the better results are obtained after the method is improved. GA relies on the Chinese character itself and context information. In improved method, the parameters of single character recognition (SCR) model are conditional probabilities that can be converted into posterior probabilities by theoretic analysis. On the basis of SCR output analysis, posterior probabilities of candidates are obtained by statistical method. Experiment shows that post-processing performance depends on efficient evaluation of posterior probability, besides proper method. The character recognition average rate of the tested texts is from 96.93% to 97.98%, from 96.93% to 98.47% after improvement, and improves 0.49%.
  • Keywords
    Markov processes; character recognition; genetic algorithms; natural languages; reliability; statistical analysis; CCRP; Chinese character recognition post processing; Markov processes; SCR model; conditional probabilities; context information; genetic algorithms; posterior probabilities; reliability; single character recognition model; statistical methods; system parameter optimisation; Character recognition; Genetic algorithms; Genetic mutations; Handwriting recognition; Information science; Mathematics; Natural languages; Probability; Text recognition; Thyristors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2003 International Conference on
  • Print_ISBN
    0-7803-8131-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2003.1260105
  • Filename
    1260105