Stochastic model of stroke order variation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

A stochastic model of stroke order variation is proposed and applied to the stroke-order free on-line Kanji character recognition. The proposed model is a hidden Markov model (HMM) with a special topology to represent all stroke order variations. A sequence of state transitions from the initial state to the final state of the model represents one stroke order and provides a probability of the stroke order. The distribution of the stroke order probability can be trained automatically by using an EM algorithm from a training set of on-line character patterns. Experimental results on large-scale test patterns showed that the proposed model could represent actual stroke order variations appropriately and improve recognition accuracy by penalizing incorrect stroke orders.

Original languageEnglish
Title of host publicationICDAR2009 - 10th International Conference on Document Analysis and Recognition
Pages803-807
Number of pages5
DOIs
Publication statusPublished - 2009
EventICDAR2009 - 10th International Conference on Document Analysis and Recognition - Barcelona, Spain
Duration: Jul 26 2009Jul 29 2009

Other

OtherICDAR2009 - 10th International Conference on Document Analysis and Recognition
CountrySpain
CityBarcelona
Period7/26/097/29/09

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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