Title :
Recognition of multifont Farsi/Arabic characters using a fuzzy neural network
Author :
Namazi, Mehdi ; Faez, Karim
Author_Institution :
Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran
Abstract :
In this paper an algorithm is developed for recognition of printed Farsi characters with various fonts, irrespective of size, rotation and stork. The system uses pseudo-Zernike moments as input features and the classifier consists of a complex of neural networks (NN) and fuzzy neural networks (FNN). The advantage of using FNN is it´s ability to classify similar patterns. The performance of the system is evaluated on a database consisting of more than 3700 character samples. The achieved accuracy is 99.85%
Keywords :
fuzzy neural nets; image classification; optical character recognition; classifier; fuzzy neural network; input features; multifont Arabic characters; multifont Farsi characters; performance; printed Farsi characters; pseudo-Zernike moments; recognition; Character recognition; Feature extraction; Feedforward neural networks; Fuzzy neural networks; Fuzzy set theory; Fuzzy sets; Neural networks; Pattern recognition; Polynomials; Spatial databases;
Conference_Titel :
TENCON '96. Proceedings., 1996 IEEE TENCON. Digital Signal Processing Applications
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-3679-8
DOI :
10.1109/TENCON.1996.608470
