Arabic text recognition using steady state genetic algorithm

Abdullah M. Al-Mutawa; Chilukuri K. Mohan

Arabic text recognition using steady state genetic algorithm

Abdullah M. Al-Mutawa, Chilukuri K. Mohan

Research output: Contribution to journal › Article › peer-review

Abstract

Text recognition is a challenging task when text contains overlapping and cursive characters. This paper addresses the task of matching input Arabic text with character forms in the database described in terms of features. We introduce new features that approximate character shapes, designed to make the recognition process independent of font size. We apply steady state genetic algorithms to the partial shape-matching task, with extremely encouraging recognition rate. No separate segmentation is required in our approach.

Original language	English (US)
Pages (from-to)	311-316
Number of pages	6
Journal	Intelligent Engineering Systems Through Artificial Neural Networks
Volume	1998
State	Published - 1998
Externally published	Yes

Keywords

Character Recognition
Edge Detection
Feature Extraction
Genetic Algorithms

ASJC Scopus subject areas

Software

Cite this

@article{9e209325f3874671807706bf34cb7451,

title = "Arabic text recognition using steady state genetic algorithm",

abstract = "Text recognition is a challenging task when text contains overlapping and cursive characters. This paper addresses the task of matching input Arabic text with character forms in the database described in terms of features. We introduce new features that approximate character shapes, designed to make the recognition process independent of font size. We apply steady state genetic algorithms to the partial shape-matching task, with extremely encouraging recognition rate. No separate segmentation is required in our approach.",

keywords = "Character Recognition, Edge Detection, Feature Extraction, Genetic Algorithms",

author = "Al-Mutawa, {Abdullah M.} and Mohan, {Chilukuri K.}",

year = "1998",

language = "English (US)",

volume = "1998",

pages = "311--316",

journal = "Intelligent Engineering Systems Through Artificial Neural Networks",

publisher = "The American Society of Mechanical Engineers(ASME)",

}

TY - JOUR

T1 - Arabic text recognition using steady state genetic algorithm

AU - Al-Mutawa, Abdullah M.

AU - Mohan, Chilukuri K.

PY - 1998

Y1 - 1998

N2 - Text recognition is a challenging task when text contains overlapping and cursive characters. This paper addresses the task of matching input Arabic text with character forms in the database described in terms of features. We introduce new features that approximate character shapes, designed to make the recognition process independent of font size. We apply steady state genetic algorithms to the partial shape-matching task, with extremely encouraging recognition rate. No separate segmentation is required in our approach.

AB - Text recognition is a challenging task when text contains overlapping and cursive characters. This paper addresses the task of matching input Arabic text with character forms in the database described in terms of features. We introduce new features that approximate character shapes, designed to make the recognition process independent of font size. We apply steady state genetic algorithms to the partial shape-matching task, with extremely encouraging recognition rate. No separate segmentation is required in our approach.

KW - Character Recognition

KW - Edge Detection

KW - Feature Extraction

KW - Genetic Algorithms

UR - http://www.scopus.com/inward/record.url?scp=0032291748&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032291748&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0032291748

VL - 1998

SP - 311

EP - 316

JO - Intelligent Engineering Systems Through Artificial Neural Networks

JF - Intelligent Engineering Systems Through Artificial Neural Networks

ER -

Arabic text recognition using steady state genetic algorithm

Abstract

Keywords

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this