Efficient segmentation of Arabic handwritten characters using structural features
Joint Authors
Bahashwan, Mazin
Abu Bakr, Sayyid
Shaykh, Uthman
Source
The International Arab Journal of Information Technology
Issue
Vol. 14, Issue 6 (30 Nov. 2017)10 p.
Publisher
Publication Date
2017-11-30
Country of Publication
Jordan
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Handwriting recognition is an important field as it has many practical applications such as for bank cheque processing, post office address processing and zip code recognition.
Most applications are developed exclusively for Latin characters.
However, despite tremendous effort by researchers in the past three decades, Arabic handwriting recognition accuracy remains low because of low efficiency in determining the correct segmentation points.
This paper presents an approach for character segmentation of unconstrained handwritten Arabic words.
First, we seek all possible character segmentation points based on structural features.
Next, we develop a novel technique to create several paths for each possible segmentation point.
These paths are used in differentiating between different types of segmentation points.
Finally, we use heuristic rules and neural networks, utilizing the information related to segmentation points, to select the correct segmentation points.
For comparison, we applied our method on IESK-arDB and IFN/ENIT databases, in which we achieved a success rate of 91.6% and 90.5% respectively
American Psychological Association (APA)
Bahashwan, Mazin& Abu Bakr, Sayyid& Shaykh, Uthman. 2017. Efficient segmentation of Arabic handwritten characters using structural features. The International Arab Journal of Information Technology،Vol. 14, no. 6.
https://search.emarefa.net/detail/BIM-853070
Modern Language Association (MLA)
Bahashwan, Mazin…[et al.]. Efficient segmentation of Arabic handwritten characters using structural features. The International Arab Journal of Information Technology Vol. 14, no. 6 (Nov. 2017).
https://search.emarefa.net/detail/BIM-853070
American Medical Association (AMA)
Bahashwan, Mazin& Abu Bakr, Sayyid& Shaykh, Uthman. Efficient segmentation of Arabic handwritten characters using structural features. The International Arab Journal of Information Technology. 2017. Vol. 14, no. 6.
https://search.emarefa.net/detail/BIM-853070
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-853070