Off-line Arabic character recognition: the state of the art

Machine simulation of human reading has been the subject of intensive research for almost three decades. A large number of research papers and reports have already been published on Latin, Chinese and Japanese characters. However, little work has been conducted on the automatic recognition of Arabic...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Pattern recognition Jg. 31; H. 5; S. 517 - 530
1. Verfasser: Amin, Adnan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 01.05.1998
Elsevier Science
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Arabic characters
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Feature extraction
Handwriting recognition
Hidden Markov Models
Neural Network classifiers
Off-line recognition
Optical character recognition
Pattern recognition. Digital image processing. Computational geometry
Problem solving, game playing
Segmentation
Theoretical computing
Handwriting recognition
Off-line recognition
Segmentation
Optical character recognition
Neural Network classifiers
Feature extraction
Hidden Markov Models
Arabic characters
Markov chain
Arabic
Classification
Pattern recognition
Neural network
Manuscript character
Learning algorithm
Pattern extraction
ISSN:0031-3203, 1873-5142
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Machine simulation of human reading has been the subject of intensive research for almost three decades. A large number of research papers and reports have already been published on Latin, Chinese and Japanese characters. However, little work has been conducted on the automatic recognition of Arabic characters because of the complexity of printed and handwritten text, and this problem is still an open research field. The main objective of this paper is to present the state of Arabic character recognition research throughout the last two decades.
ISSN:0031-3203
1873-5142
DOI:10.1016/S0031-3203(97)00084-8