본문 바로가기
  • Home

Improving On-line Handwritten Character Recognition with Hidden Markov Model

  • Journal of Knowledge Information Technology and Systems
  • Abbr : JKITS
  • 2011, 6(1), pp.61-68
  • Publisher : Korea Knowledge Information Technology Society
  • Research Area : Interdisciplinary Studies > Interdisciplinary Research
  • Published : February 28, 2011

마명 1 Park Dong-Won 1

1배재대학교

Candidate

ABSTRACT

Method for on-line handwritten character recognition with Hidden Markov Model(HMM) is proposed. To deal with the problem of handwriting style variations, the Hierarchical Clustering approach is introduced to partition different writing styles into several classes. One HMM that models temporal and spatial variability of handwriting is constructed based on each class. Therefore a multiple modeling technique is used for both Korean and English characters. The HMMs of Korean graphemes are concatenated to form the Korean character models. The recognition of handwriting characters is implemented by a modified level building algorithm, which incorporates the Korean character combination rules within the efficient network search procedure. Due to the limitation of HMM based method, a post-processing procedure which takes the global and structural features into account is proposed. Experiments showed the proposed recognition system which uses multiple modeling technique, the modified level building algorithm and the post-processing procedure achieved high writer independent recognition rate on unconstrained samples of both English and Korean characters. The comparison with other schemes of HMM-based recognition is also performed to evaluate the system.

Citation status

* References for papers published after 2023 are currently being built.