Arabic handwritten word recognition based on dynamic Bayesian network
Joint Authors
Bin Imarah, Najwa
Jayech, Khawlah
Source
The International Arab Journal of Information Technology
Issue
Vol. 13, Issue 6B (31 Dec. 2016), pp.1024-1031, 8 p.
Publisher
Publication Date
2016-12-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Arabic language and Literature
Topics
Abstract EN
Distinguishing an Arabic handwritten text is a hard task because the Arabic word is morphologically complex and the writing style from one model is highly variable, like the recognition of words representing the names of Tunisian cities.
Actually, this is the first work based on the Dynamic Hierarchical Bayesian Network (DHBN).
Its objective is to get the best model by learning the structure and parameter of Arabic handwriting to decrease the complexity of the recognition process by allowing the partial recognition.
In fact, we propose segmenting the word based on a vertical smoothed histogram projection using various width values to put down the segmentation error.
After that, we extract the characteristics of each cell using the Zernike and HU moments, which are invariant to rotation, translation and scaling.
Then, the sub-character is estimated at the lowest level of the Bayesian Network (BN) and the character is estimated at the highest level of the BN.
The overall Arabic words are processed by a dynamWB^ Our approach is tested using the IFN/ENIT database, where the experiment results are very promising.
American Psychological Association (APA)
Jayech, Khawlah& Bin Imarah, Najwa. 2016. Arabic handwritten word recognition based on dynamic Bayesian network. The International Arab Journal of Information Technology،Vol. 13, no. 6B, pp.1024-1031.
https://search.emarefa.net/detail/BIM-655312
Modern Language Association (MLA)
Jayech, Khawlah& Bin Imarah, Najwa. Arabic handwritten word recognition based on dynamic Bayesian network. The International Arab Journal of Information Technology Vol. 13, no. 6B (2016), pp.1024-1031.
https://search.emarefa.net/detail/BIM-655312
American Medical Association (AMA)
Jayech, Khawlah& Bin Imarah, Najwa. Arabic handwritten word recognition based on dynamic Bayesian network. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 6B, pp.1024-1031.
https://search.emarefa.net/detail/BIM-655312
Data Type
Journal Articles
Language
English
Notes
Includes appendix.
Record ID
BIM-655312