Recognition of printed Arabic words with fuzzy ARTMAP neural network

Author

Amin, Adnan ; Murshed, Nabeel

Author_Institution

Sch. of Comput. Sci. & Eng., New South Wales Univ., Sydney, NSW, Australia

Volume

4

fYear

1999

fDate

1999

Firstpage

2903

Abstract

This paper presents a new method for the recognition of Arabic text using global features and fuzzy ARTMAP neural network. The method is divided into three major steps. The first step is digitization and pre-processing to create connected component. The second step is concerned with feature extraction, where global features of the input word are used to extract features such as number of subwords, number of peaks within the subword, number and position of the complementary character, etc., to avoid the difficulty of segmentation stage. The third step is the classification and is composed of a single fuzzy ARTMAP. The method was evaluated with 3255 images of 217 Arabic words with different fonts (each word has 15 samples), and the mean correct classification rate was 95.25%

Keywords

ART neural nets; character recognition; feature extraction; fuzzy neural nets; image classification; image segmentation; ARTMAP neural network; Arabic text recognition; digitization; feature extraction; fuzzy neural network; image classification; image segmentation; Character recognition; Computer science; Fuzzy neural networks; Humans; Image segmentation; Neural networks; Optical character recognition software; Optical noise; Optical recording; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.833546

Filename

833546