Modeling and recognition of cursive words with hidden Markov models

Wongyu Cho, Seong Whan Lee, Jin H. Kim

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)


In this paper, a new method for modeling and recognizing cursive words with hidden Markov models (HMM) is presented. In the proposed method, a sequence of thin fixed-width vertical frames are extracted from the image, capturing the local features of the handwriting. By quantizing the feature vectors of each frame, the input word image is represented as a Markov chain of discrete symbols. A handwritten word is regarded as a sequence of characters and optional ligatures. Hence, the ligatures are also explicitly modeled. With this view, an interconnection network of character and ligature HMMs is constructed to model words of indefinite length. This model can ideally describe any form of handwritten words, including discretely spaced words, pure cursive words and unconstrained words of mixed styles. Experiments have been conducted with a standard database to evaluate the performance of the overall scheme. The performance of various search strategies based on the forward and backward score has been compared. Experiments on the use of a preclassifier based on global features show that this approach may be useful for even large-vocabulary recognition tasks.

Original languageEnglish
Pages (from-to)1941-1953
Number of pages13
JournalPattern Recognition
Issue number12
Publication statusPublished - 1995 Dec


  • Cursive script
  • Handwritten word recognition
  • Hidden Markov models
  • Ligature modeling
  • Principal component analysis
  • Viterbi search

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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