A combined method for Persian and Arabic handwritten digit recognition

Author

Hosseini, Habib Mir Mohammad ; Bouzerdoum, Abdesselam

fYear

1996

fDate

18-20 Nov 1996

Firstpage

80

Lastpage

83

Abstract

A method for recognition of unconstrained Persian and Arabic handwritten digits is introduced. In the proposed algorithm, after thinning the binary image of the digit, the character matrix is divided into several segments. Each segment is scanned by a vertical and a horizontal raster to find the number of crossings between the raster lines and the character body. The resulting feature vector, which has a length of 10, is applied to a multilayer perceptron (MLP) trained by the backpropagation learning technique. The rate of correct classification using the MLP was 81%. The recognition rate of the system was then increased by combining the output of the neural network classifier with the output of simple classifiers which are specially designed to distinguish between similar digits. The combination of these classifiers with the MLP increased the recognition rate of the system to 92%

Keywords

backpropagation; handwriting recognition; multilayer perceptrons; natural languages; pattern classification; Arabic handwritten digit recognition; MLP; Persian handwritten digit recognition; backpropagation learning technique; binary image; character body; character matrix; combined method; feature vector; horizontal raster; multilayer perceptron; neural network classifier; raster lines; recognition rate; Australia; Backpropagation; Character recognition; Handwriting recognition; Image segmentation; Information processing; Multilayer perceptrons; Neural networks; Shape; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Systems, 1996., Australian and New Zealand Conference on

Conference_Location

Adelaide, SA

Print_ISBN

0-7803-3667-4

Type

conf

DOI

10.1109/ANZIIS.1996.573894

Filename

573894