Occluded character restoration using active contour with shape priors

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

6 Scopus citations

Abstract

Broken or partially visible characters is common phenomenon in historical documents. It stems from various factors, such as overlaid text or degradation. Restoring such characters is necessary for document analysis applications. This paper presents a new approach for restoring underlaying Hebrew broken characters that were partially occluded by Arabic text in a palimpsest. We apply text recognition on the fragments of the Hebrew letters and select the k candidate letters that match best the fragments of the Hebrew letters. We then complete the broken Hebrew characters using active contour with the k candidates as shape priors. We use a modified geodesic active contour, which we tailored to occluded text restoration. It is initialized on the fragments of the Hebrew text, then it undergoes an expansion phase and a contraction phase via the occluding Arabic text to form the restored Hebrew character. We measure the distance between the completed character and its corresponding priors and choose the shape with the minimal distance as the reconstructed character. Experimental results are presented. On average 68% of the characters were correctly reconstructed.

Original languageEnglish
Title of host publicationProceedings - 13th International Conference on Frontiers in Handwriting Recognition, ICFHR 2012
Pages497-502
Number of pages6
DOIs
StatePublished - 1 Dec 2012
Event13th International Conference on Frontiers in Handwriting Recognition, ICFHR 2012 - Bari, Italy
Duration: 18 Sep 201220 Sep 2012

Publication series

NameProceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR
ISSN (Print)1550-5235

Conference

Conference13th International Conference on Frontiers in Handwriting Recognition, ICFHR 2012
Country/TerritoryItaly
CityBari
Period18/09/1220/09/12

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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