Layout analysis for Arabic historical document images using machine learning

Syed Saqib Bukhari, Thomas M. Breuel, Abedelkadir Asi, Jihad El-Sana

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

46 Scopus citations

Abstract

Page layout analysis is a fundamental step of any document image understanding system. We introduce an approach that segments text appearing in page margins (a.k.a side-notes text) from manuscripts with complex layout format. Simple and discriminative features are extracted in a connected-component level and subsequently robust feature vectors are generated. Multilayer perception classifier is exploited to classify connected components to the relevant class of text. A voting scheme is then applied to refine the resulting segmentation and produce the final classification. In contrast to state-of-the-art segmentation approaches, this method is independent of block segmentation, as well as pixel level analysis. The proposed method has been trained and tested on a dataset that contains a variety of complex side-notes layout formats, achieving a segmentation accuracy of about 95%.

Original languageEnglish
Title of host publicationProceedings - 13th International Conference on Frontiers in Handwriting Recognition, ICFHR 2012
Pages639-644
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

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