Arabic Document Image Classification Using Neural Networks.
Al-Khorabi, A., Mansour, M. (2020). Arabic Document Image Classification Using Neural Networks.. EKB Journal Management System, 29(1), 56-63. doi: 10.21608/bfemu.2020.132761
Abdallah Al-Khorabi; Mohamed Abduallah Mansour. "Arabic Document Image Classification Using Neural Networks.". EKB Journal Management System, 29, 1, 2020, 56-63. doi: 10.21608/bfemu.2020.132761
Al-Khorabi, A., Mansour, M. (2020). 'Arabic Document Image Classification Using Neural Networks.', EKB Journal Management System, 29(1), pp. 56-63. doi: 10.21608/bfemu.2020.132761
Al-Khorabi, A., Mansour, M. Arabic Document Image Classification Using Neural Networks.. EKB Journal Management System, 2020; 29(1): 56-63. doi: 10.21608/bfemu.2020.132761

Arabic Document Image Classification Using Neural Networks.

MEJ- Mansoura Engineering Journal
Article 18, Volume 29, Issue 1, March 2004, Page 56-63  XML PDF (218.14 K)
Document Type: Research Studies
DOI: 10.21608/bfemu.2020.132761
View on SCiNiTO View on SCiNiTO
Authors
Abdallah Al-Khorabi email 1; Mohamed Abduallah Mansourorcid 2
1Sana'a University, Sana'a, Yemen Republic P. 0. Box 1341, Fax 967-1-2505 14
2Postgraduate Student University of Science and Technology Sana'a, Yemen Republic P, 0, Box 1341
Abstract
The Neural Network Arabic Document Image Classification System (NNADICS) is an adaptive Arabic document classifier. By training NNADICS on a number of different document image types, NNADICS behaves as a multiple classifier, since it is capable for distinguishing between multiple document image types. NNADICS is designed, built, tested and evaluated. After training NNADICS a document image is applied to the system for classification. Before that the document image is scanned, pre-processed and binarized, and then applied to NNADICS to classify its contents to text, geometric, or photographic image type. NNADICS achieved an average of a 86% recognition rate as it is clearly demonstrated.
Statistics
Article View: 103
PDF Download: 143