A hybrid approach for multifont Arabic characters recognition

Authors:
Nadia Ben Amor;Najoua Essoukri Ben Amara
Affiliations:
National Engineering School of Tunis, Tunisia;National Engineering School of Monastir, Tunisia
Venue:
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Year:
2006

Citing 0
Cited 3

Off-line cursive handwritten Tamil character recognition

WSEAS Transactions on Signal Processing
Benchmark database and GUI environment for printed Arabic text recognition research

WSEAS Transactions on Information Science and Applications
Offline arabic handwritten text recognition: A Survey

ACM Computing Surveys (CSUR)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Pattern recognition is a well-established field of study and Optical Character Recognition (OCR) has long been seen as one of its important contributions. In this paper we describe the performances of a hybrid classification approach which combines both neural networks and hidden Markov models. This classification technique is dealing with features extracted through the wavelet transform method. Experimental tests have been carried out on a set of 85.000 samples of characters corresponding to 5 different Arabic fonts. Some promising experimental results are reported.