Hybrid model of post-processing techniques for Arabic optical character recognition

Optical character recognition (OCR) is used to extract text contained in an image. One of the stages in OCR is the post-processing and it corrects the errors of OCR output text. The OCR multiple outputs approach consists of three processes: differentiation, alignment, and voting. Existing differenti...

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Main Author:	Habeeb, Imad Qasim
Format:	Thesis
Language:	eng eng
Published:	2016
Subjects:	T58.5-58.64 Information technology
Online Access:	https://etd.uum.edu.my/6030/1/s94758_01.pdf https://etd.uum.edu.my/6030/2/s94758_02.pdf
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id	my-uum-etd.6030
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Mohd Yusof, Shahrul Azmi Yusof, Yuhanis
topic	T58.5-58.64 Information technology T58.5-58.64 Information technology
spellingShingle	T58.5-58.64 Information technology T58.5-58.64 Information technology Habeeb, Imad Qasim Hybrid model of post-processing techniques for Arabic optical character recognition
description	Optical character recognition (OCR) is used to extract text contained in an image. One of the stages in OCR is the post-processing and it corrects the errors of OCR output text. The OCR multiple outputs approach consists of three processes: differentiation, alignment, and voting. Existing differentiation techniques suffer from the loss of important features as it uses N-versions of input images. On the other hand, alignment techniques in the literatures are based on approximation while the voting process is not context-aware. These drawbacks lead to a high error rate in OCR. This research proposed three improved techniques of differentiation, alignment, and voting to overcome the identified drawbacks. These techniques were later combined into a hybrid model that can recognize the optical characters in the Arabic language. Each of the proposed technique was separately evaluated against three other relevant existing techniques. The performance measurements used in this study were Word Error Rate (WER), Character Error Rate (CER), and Non-word Error Rate (NWER). Experimental results showed a relative decrease in error rate on all measurements for the evaluated techniques. Similarly, the hybrid model also obtained lower WER, CER, and NWER by 30.35%, 52.42%, and 47.86% respectively when compared to the three relevant existing models. This study contributes to the OCR domain as the proposed hybrid model of post-processing techniques could facilitate the automatic recognition of Arabic text. Hence, it will lead to a better information retrieval.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Habeeb, Imad Qasim
author_facet	Habeeb, Imad Qasim
author_sort	Habeeb, Imad Qasim
title	Hybrid model of post-processing techniques for Arabic optical character recognition
title_short	Hybrid model of post-processing techniques for Arabic optical character recognition
title_full	Hybrid model of post-processing techniques for Arabic optical character recognition
title_fullStr	Hybrid model of post-processing techniques for Arabic optical character recognition
title_full_unstemmed	Hybrid model of post-processing techniques for Arabic optical character recognition
title_sort	hybrid model of post-processing techniques for arabic optical character recognition
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2016
url	https://etd.uum.edu.my/6030/1/s94758_01.pdf https://etd.uum.edu.my/6030/2/s94758_02.pdf
_version_	1747828010471391232
spelling	my-uum-etd.60302021-04-05T02:28:59Z Hybrid model of post-processing techniques for Arabic optical character recognition 2016 Habeeb, Imad Qasim Mohd Yusof, Shahrul Azmi Yusof, Yuhanis Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences T58.5-58.64 Information technology QA75 Electronic computers. Computer science Optical character recognition (OCR) is used to extract text contained in an image. One of the stages in OCR is the post-processing and it corrects the errors of OCR output text. The OCR multiple outputs approach consists of three processes: differentiation, alignment, and voting. Existing differentiation techniques suffer from the loss of important features as it uses N-versions of input images. On the other hand, alignment techniques in the literatures are based on approximation while the voting process is not context-aware. These drawbacks lead to a high error rate in OCR. This research proposed three improved techniques of differentiation, alignment, and voting to overcome the identified drawbacks. These techniques were later combined into a hybrid model that can recognize the optical characters in the Arabic language. Each of the proposed technique was separately evaluated against three other relevant existing techniques. The performance measurements used in this study were Word Error Rate (WER), Character Error Rate (CER), and Non-word Error Rate (NWER). Experimental results showed a relative decrease in error rate on all measurements for the evaluated techniques. Similarly, the hybrid model also obtained lower WER, CER, and NWER by 30.35%, 52.42%, and 47.86% respectively when compared to the three relevant existing models. This study contributes to the OCR domain as the proposed hybrid model of post-processing techniques could facilitate the automatic recognition of Arabic text. Hence, it will lead to a better information retrieval. 2016 Thesis https://etd.uum.edu.my/6030/ https://etd.uum.edu.my/6030/1/s94758_01.pdf text eng public https://etd.uum.edu.my/6030/2/s94758_02.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia AbdelRaouf, A., Higgins, C. A., Pridmore, T., & Khalil, M. (2010). Building a multimodal Arabic corpus (MMAC). International Journal on Document Analysis and Recognition (IJDAR), 13(4), 285-302. Abdulkader, A. E., & Casey, M. R. (2015). Efficient identification and correction of optical character recognition errors through learning in a multi-engine environment: Google Patents. Abulnaja, O. A., & Batawi, Y. A. (2012). Improving Arabic Optical Character Recognition Accuracy Using N-Version Programming Technique. Canadian Journal on Image Processing and Computer Vision, 3(2), 44-46. Ahmad, I., Mahmoud, S. A., & Fink, G. A. (2016). Open-vocabulary recognition of machine-printed Arabic text using hidden Markov models. 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Hybrid model of post-processing techniques for Arabic optical character recognition

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