Making scanned Arabic documents machine accessible using an ensemble of SVM classifiers-Reference-Cited by-同舟云学术

Making scanned Arabic documents machine accessible using an ensemble of SVM classifiers

Published:2018-04-02 Issue:1-2 Volume:21 Page:59-75
ISSN:1433-2833
Container-title:International Journal on Document Analysis and Recognition (IJDAR)
language:en
Short-container-title:IJDAR

Author:

Elanwar Randa^ORCID,Qin Wenda,Betke Margrit

Funder

National Science Foundation

Cairo Initiative Scholarship Program

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/article/10.1007/s10032-018-0298-x/fulltext.html

Reference65 articles.

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3. Alshameri, A., Abdou, S., Mostafa, K.: A combined algorithm for layout analysis of Arabic document images and text lines extraction. Int. J. Comput. Appl. 49(23), 30–37 (2012)

4. Bukhari, S.S., Breuel, T.M., Asi, A., El-Sana, J.: Layout analysis for Arabic historical document images using machine learning. In: International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 639–644 (2012)

5. Hadjar, K., Ingold, R.: Physical layout analysis of complex structured Arabic documents using artificial neural nets. In: International Workshop on Document Analysis Systems, pp. 170–178 (2004)

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