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Online handwritten cursive word recognition using segmentation-free and segmentation-based methods

  • Tokyo University of Agriculture and Technology
  • SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper describes a comparison between online handwritten cursive word recognition using segmentation-free method and that using segmentation-based method. To search the optimal segmentation and recognition path as the recognition result, we attempt two methods: segmentation-free and segmentation-based, where we expand the search space using a character-synchronous beam search strategy. The probable search paths are evaluated by integrating character recognition scores with geometric characteristics of the character patterns in a Conditional Random Field (CRF) model. Our methods restrict the search paths from the trie lexicon of words and preceding paths during path search. We show this comparison on a publicly available dataset (lAM-OnDB).

Original languageEnglish
Title of host publicationProceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages161-165
Number of pages5
ISBN (Electronic)9781479961009
DOIs
StatePublished - Jun 7 2016
Event3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 - Kuala Lumpur, Malaysia
Duration: Nov 3 2016Nov 6 2016

Publication series

NameProceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

Conference

Conference3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
Country/TerritoryMalaysia
CityKuala Lumpur
Period11/3/1611/6/16

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