Learning non-Markovian constraints for handwriting recognition

Ryosuke Kakisako, Seiichi Uchida, Frinken Volkmar

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

Abstract

Recently, the horizon of dynamic time warping (DTW) for matching two sequential patterns has been extended to deal with non-Markovian constraints. The non-Markovian constraints regulate the matching in a wider scale, whereas Markovian constraints regulate the matching only locally. The global optimization of the non-Markovian DTW is proved to be solvable in polynomial time by a graph cut algorithm. The main contribution of this paper is to reveal what is the best constraint for handwriting recognition by using the non-Markovian DTW. The result showed that the best constraint is not a Markovian but a totally non-Markovian constraint that regulates the matching between very distant points; that is, it was proved that the conventional Markovian DTW has a clear limitation and the non- Markovian DTW should be more focused in future research.

Original languageEnglish
Title of host publication13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
PublisherIEEE Computer Society
Pages446-450
Number of pages5
Volume2015-November
ISBN (Electronic)9781479918058
DOIs
Publication statusPublished - Nov 20 2015
Event13th International Conference on Document Analysis and Recognition, ICDAR 2015 - Nancy, France
Duration: Aug 23 2015Aug 26 2015

Other

Other13th International Conference on Document Analysis and Recognition, ICDAR 2015
CountryFrance
CityNancy
Period8/23/158/26/15

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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  • Cite this

    Kakisako, R., Uchida, S., & Volkmar, F. (2015). Learning non-Markovian constraints for handwriting recognition. In 13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings (Vol. 2015-November, pp. 446-450). [7333801] IEEE Computer Society. https://doi.org/10.1109/ICDAR.2015.7333801