Paper Info
Title
Efficient Segmentation Of Arabic Handwritten Characters Using Structural Features
Abstract
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.
Year
Venue
Keywords
2017
INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY
Arabic handwriting, character segmentation and structural features
Field
DocType
Volume
Heuristic,Arabic handwriting,Arabic,Cashier's check,Pattern recognition,Computer science,Arabic handwriting recognition,Segmentation,Handwriting recognition,Artificial intelligence,Natural language processing,Artificial neural network
Journal
14
Issue
ISSN
Citations 
6
1683-3198
0
PageRank 
References 
Authors
0.34
17
3
Name
Order
Citations
PageRank
Mazen Abdullah Bahashwan100.68
S. A. R. Abu-Bakar2799.67
Usman Ullah Sheikh3498.41