Text line segmentation for challenging handwritten document images using fully convolutional network

Berat Barakat, Ahmad Droby, Majeed Kassis, Jihad El-Sana

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

26 Scopus citations

Abstract

This paper presents a method for text line segmentation of challenging historical manuscript images. These manuscript images contain narrow interline spaces with touching components, interpenetrating vowel signs and inconsistent font types and sizes. In addition, they contain curved, multi-skewed and multi-directed side note lines within a complex page layout. Therefore, bounding polygon labeling would be very difficult and time consuming. Instead we rely on line masks that connect the components on the same text line. Then these line masks are predicted using a Fully Convolutional Network (FCN). In the literature, FCN has been successfully used for text line segmentation of regular handwritten document images. The present paper shows that FCN is useful with challenging manuscript images as well. Using a new evaluation metric that is sensitive to over segmentation as well as under segmentation, testing results on a publicly available challenging handwritten dataset are comparable with the results of a previous work on the same dataset.

Original languageEnglish
Title of host publicationProceedings - 2018 16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018
PublisherInstitute of Electrical and Electronics Engineers
Pages374-379
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
ISSN (Print)2167-6445
ISSN (Electronic)2167-6453

Conference

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

Keywords

  • Fully convolutional network
  • challenging historical document
  • text line segmentation

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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