Paper Info
Title
Two-Stage Lexicon Reduction For Offline Arabic Handwritten Word Recognition
Abstract
Given large number of words to be recognized, a two-stage strategy for eliminating unlikely candidates before recognition can be a reasonable and powerful approach for increasing the recognition speed. A holistic lexicon reduction technique for offline handwritten Arabic word recognition is proposed in this paper. The principle of this technique involves the extraction of dots and subwords from the cursive Arabic word image to describe its shape. In the first stage of reduction, the number of subwords in the input word is estimated. Then in the second stage, the word descriptor, based on the dots information, is used while taking into account only the candidates selected in the first stage. Experimental results on IFN/ENIT database, consisting of 26,459 cursive Arabic word images, show a lexicon reduction of 92.5% with accuracy of 74%.
Year
DOI
Venue
2008
10.1142/S0218001408006843
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
Lexicon reduction, offline Arabic handwritten word recognition, shape descriptor, string matching, IFN/ENIT database
String searching algorithm,Cursive,Arabic,Pattern recognition,Computer science,Word recognition,Speech recognition,Lexicon,Artificial intelligence,Natural language processing,Intelligent word recognition
Journal
Volume
Issue
ISSN
22
7
0218-0014
Citations 
PageRank 
References 
8
0.65
15
Authors
4
Name
Order
Citations
PageRank
Saeed Mozaffari115214.19
Karim Faez281983.23
Volker Märgner329529.02
Haikal El-Abed443629.39