Multi-perspective multi-modal trajectory descriptions for handwritten strokes

Mohammad Tanvir Parvez, Sardar Anisul Haque

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

2 Scopus citations

Abstract

This paper presents a novel approach to generate bag-of-words model based trajectory descriptions for handwritten strokes. We demonstrate how multiple distinct representations can be generated for the same stroke to accommodate writing variations and capture local features at stroke-segment level. The proposed descriptions can be utilized in template matching for handwriting recognition/correction, writer identification, signature verification, etc. The suitability of the proposed shape representations is experimented in a number of settings and used to build a language independent pen-based system for handwriting learning with feedback.

Original languageEnglish
Title of host publicationProceedings - 2018 16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages285-290
Number of pages6
ISBN (Electronic)9781538658758
DOIs
StatePublished - 5 Dec 2018
Event16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018 - Niagara Falls, United States
Duration: 5 Aug 20188 Aug 2018

Publication series

NameProceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
Volume2018-August

Conference

Conference16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018
Country/TerritoryUnited States
CityNiagara Falls
Period5/08/188/08/18

Keywords

  • pen-based systems
  • shape descriptors
  • stroke correction
  • trajectory representation

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