research-article

Gabor features for offline Arabic handwriting recognition

Authors:

Anurag Bhardwaj,

Prem NatarajanAuthors Info & Claims

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

Pages 53 - 58

https://doi.org/10.1145/1815330.1815337

Published: 09 June 2010 Publication History

Abstract

Many feature extraction approaches for off-line handwriting recognition (OHR) rely on accurate binarization of gray-level images. However, high-quality binarization of most real-world documents is extremely difficult due to varying characteristics of noises artifacts common in such documents. Unlike most of these features, Gabor features do not require binarization of the document images, and thus are likely to be more robust to noises in document images. To demonstrate the efficacy of our proposed Gabor features, we perform subword recognition for off-line Arabic handwritten images using Support Vector Machines (SVM). We also compare the recognition performance with other binarization based features which have been proven to be effective in capturing shape characteristics of handwritten Arabic subwords, such as GSC (a set of gradient, structure, and concavity features) and skeleton based Graph features. Our preliminary experimental results show that Gabor features outperform Graph features and are slightly better than GSC features for Arabic subword recognition. In addition, by combining Gabor and GSC features, we obtain a significant reduction in classification error rate over using GSC or Gabor features alone.

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Cited By

Khan SNazir SKhan H(2023)Analysis of Cursive Text Recognition Systems: A Systematic Literature ReviewACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359260022:7(1-30)Online publication date: 20-Jul-2023
https://dl.acm.org/doi/10.1145/3592600
Adam KAl-Maadeed SAkbari Y(2022)Hierarchical Fusion Using Subsets of Multi-Features for Historical Arabic Manuscript DatingJournal of Imaging10.3390/jimaging80300608:3(60)Online publication date: 1-Mar-2022
https://doi.org/10.3390/jimaging8030060
Zafar AIqbal A(2022)Application of soft computing techniques in machine reading of Quranic Kufic manuscriptsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2020.04.01734:6(3062-3069)Online publication date: Jun-2022
https://doi.org/10.1016/j.jksuci.2020.04.017
Show More Cited By

Index Terms

Gabor features for offline Arabic handwriting recognition
1. Computing methodologies
  1. Machine learning

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cover image ACM Other conferences

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

June 2010

490 pages

ISBN:9781605587738

DOI:10.1145/1815330

General Chairs:
David Doermann
University of Maryland, College Park
,
Venu Govindaraju
University at Buffalo, SUNY
,
Daniel Lopresti
Lehigh University
,
Prem Natarajan
Raytheon BBN Technologies

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 June 2010

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DAS '10

DAS '10: The Eighth IAPR International Workshop on Document Analysis Systems

June 9 - 11, 2010

Massachusetts, Boston, USA

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Cited By

Khan SNazir SKhan H(2023)Analysis of Cursive Text Recognition Systems: A Systematic Literature ReviewACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359260022:7(1-30)Online publication date: 20-Jul-2023
https://dl.acm.org/doi/10.1145/3592600
Adam KAl-Maadeed SAkbari Y(2022)Hierarchical Fusion Using Subsets of Multi-Features for Historical Arabic Manuscript DatingJournal of Imaging10.3390/jimaging80300608:3(60)Online publication date: 1-Mar-2022
https://doi.org/10.3390/jimaging8030060
Zafar AIqbal A(2022)Application of soft computing techniques in machine reading of Quranic Kufic manuscriptsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2020.04.01734:6(3062-3069)Online publication date: Jun-2022
https://doi.org/10.1016/j.jksuci.2020.04.017
Djaghbellou SAttia ABouziane AAkhtar Z(2022)Local features enhancement using deep auto-encoder scheme for the recognition of the proposed handwritten Arabic-Maghrebi characters databaseMultimedia Tools and Applications10.1007/s11042-022-13032-681:22(31553-31571)Online publication date: 9-Apr-2022
https://doi.org/10.1007/s11042-022-13032-6
Montaha SAzam SRafid AGhosh PHasan MJonkman MDe Boer F(2021)BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography ImagesBiology10.3390/biology1012134710:12(1347)Online publication date: 17-Dec-2021
https://doi.org/10.3390/biology10121347
Lamtougui HMoubtahij HFouadi HYahyaouy ASatori K(2020)Offline Arabic Handwriting Recognition Using Deep Learning: Comparative Study2020 International Conference on Intelligent Systems and Computer Vision (ISCV)10.1109/ISCV49265.2020.9204214(1-8)Online publication date: Jun-2020
https://doi.org/10.1109/ISCV49265.2020.9204214
Sahib ZNuri Ucan OTalab MAlnaseeri MMohammed AAli Sahib H(2020)Hybrid Method Using EDMS & Gabor for Shape and Texture2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)10.1109/HORA49412.2020.9152829(1-6)Online publication date: Jun-2020
https://doi.org/10.1109/HORA49412.2020.9152829
Zaafouri ASayadi M(2020)Medication Code Recognition using Convolutional Neural Network2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)10.1109/ATSIP49331.2020.9231801(1-5)Online publication date: Sep-2020
https://doi.org/10.1109/ATSIP49331.2020.9231801
Elleuch MKherallah M(2018)An Improved Arabic Handwritten Recognition System Using Deep Support Vector MachinesComputer Vision10.4018/978-1-5225-5204-8.ch025(656-678)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5204-8.ch025
Elanwar RQin WBetke M(2018)Making scanned Arabic documents machine accessible using an ensemble of SVM classifiersInternational Journal on Document Analysis and Recognition10.1007/s10032-018-0298-x21:1-2(59-75)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s10032-018-0298-x
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