Learning non-Markovian constraints for handwriting recognition

Ryosuke Kakisako, Seiichi Uchida, Frinken Volkmar

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

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.

本文言語英語
ホスト出版物のタイトル13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
出版社IEEE Computer Society
ページ446-450
ページ数5
ISBN(電子版)9781479918058
DOI
出版ステータス出版済み - 11月 20 2015
イベント13th International Conference on Document Analysis and Recognition, ICDAR 2015 - Nancy, フランス
継続期間: 8月 23 20158月 26 2015

出版物シリーズ

名前Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
2015-November
ISSN(印刷版)1520-5363

その他

その他13th International Conference on Document Analysis and Recognition, ICDAR 2015
国/地域フランス
CityNancy
Period8/23/158/26/15

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識

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