NDLI: Neuro-classification for handwritten Arabic text

Content Provider	IEEE Xplore Digital Library
Author	Haraty, R.A. Ghaddar, C.
Copyright Year	2003
Description	Author affiliation: Lebanese American Univ., Beirut, Lebanon (Haraty, R.A.; Ghaddar, C.)
Abstract	Summary form only given. The issue of handwritten character recognition is still a big challenge to the scientific community. Several approaches to address this challenge have been attempted in the last years, mostly focusing on the English preprinted or handwritten characters space. Thus the need to attempt a research related to Arabic handwritten text recognition. Algorithms based on neural networks have proved to give better results than conventional methods when applied to problems where the decision rules of the classification problem are not clearly defined. Two neural networks were built to classify already segmented characters of handwritten Arabic text. The process starts by using a neuro-conventional algorithm, and a neural network for segmenting binarized connected blocks of characters; then, a heuristic algorithm extracts features from these characters and feeds them into two neural networks for classification purpose. The two neural networks correctly recognized 73% of the characters. However, one hurdle was encountered in the above scenario, which can be summarized as follows: there are a lot of handwritten characters that can be segmented and classified into two or more different classes depending on whether they are looked at separately, or in a word, or even in a sentence. In other words, character classification, especially handwritten Arabic characters, depends largely on contextual information, not only on topographic features extracted from these characters.
Sponsorship	Arab Comput. Soc. IEEE Comput. Soc
File Size	39210
File Format	PDF
ISBN	0780379837
DOI	10.1109/AICCSA.2003.1227539
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-14
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Character recognition Feature extraction Text recognition Heuristic algorithms Feeds Data mining Text categorization Artificial neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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