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International Journal on Document Analysis and Recognition (IJDAR)
Published in: International Journal on Document Analysis and Recognition (IJDAR) 4/2015

01-12-2015 | Original Paper

Nastalique segmentation-based approach for Urdu OCR

Authors: Sarmad Hussain, Salman Ali, Qurat ul Ain Akram

Published in: International Journal on Document Analysis and Recognition (IJDAR) | Issue 4/2015

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Abstract

Much work on Arabic language optical character recognition (OCR) has been on Naskh writing style. Nastalique style, used for most of languages using Arabic script across Southern Asia, is much more challenging to process due to its compactness, cursiveness, higher context sensitivity and diagonality. This makes the Nastalique writing more complex with multiple letters horizontally overlapping each other. Due to these reasons, existing methods used for Naskh would not work for Nastalique and therefore most work on Nastalique has used non-segmentation methods. The current paper presents new approach for segmentation-based analysis for Nastalique style. The paper explains the complexity of Nastalique, why Naskh based techniques cannot work for Nastalique, and proposes a segmentation-based method for developing Nastalique OCR, deriving principles and techniques for the pre-processing and recognition. The OCR is developed for Urdu language. The system is optimized using 79,093 instances of 5249 main bodies derived from a corpus of 18 million words, giving recognition accuracy of 97.11 %. The system is then tested on document images of books with 87.44 % main body recognition accuracy. The work is extensible to other languages using Nastalique.
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Footnotes
2
Based on the graphemes used for Nafees Nastalique font for Urdu.
 
3
Semi circular stroke having end point traversal path in reverse order of normal writing style.
 
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Metadata
Title
Nastalique segmentation-based approach for Urdu OCR
Authors
Sarmad Hussain
Salman Ali
Qurat ul Ain Akram
Publication date
01-12-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal on Document Analysis and Recognition (IJDAR) / Issue 4/2015
Print ISSN: 1433-2833
Electronic ISSN: 1433-2825
DOI
https://doi.org/10.1007/s10032-015-0250-2

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