A flexible framework for online document segmentation by pairwise stroke distance learning

Adrien Delaye, Kibok Lee

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

A flexible framework for handling a variety of segmentation problems in online handwritten documents is introduced. The strategy relies on single linkage clustering and a pairwise stroke distance that is globally trained for direct optimization of the segmentation. We define a variety of features that can contribute to the pairwise distance definition and show how to select a good combination of features for dealing with a new online handwritten document segmentation task. Our experiments demonstrate the validity of the method on a large range of segmentation tasks over several types of documents from various publicly available databases.

Original languageEnglish
Pages (from-to)1197-1210
Number of pages14
JournalPattern Recognition
Volume48
Issue number4
DOIs
Publication statusPublished - 2015 Apr 1

Bibliographical note

Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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