Title: Improved low-cost recognition system for handwritten Bengali numerals

Authors: Md Aktaruzzaman; Tewodros Mulugeta Dagnew; Massimo Walter Rivolta; Roberto Sassi

Addresses: Department of Computer Science and Engineering, Islamic University, Kushtia, Bangladesh ' Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy ' Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy ' Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy

Abstract: Handwriting recognition is very important due to its numerous potential applications. This paper is concerned about the low-cost features extraction for the development of an improved Bengali handwritten numeral recognition system. Each numeral was first resampled to a binary image of fixed size. A set of new features based on shape analysis was derived from the resampled image, and a multilayer neural network was trained using the extracted features. The recognition accuracy of the developed system was tested on both training and test sets of a publicly available Bengali handwritten numerals database at three different resolutions. Besides accuracy, the reliability of the system was also estimated using Cohen's kappa. The highest accuracy, 99.12% with reliability about 99%, was obtained for the test database at resolution of 32×32. The use of PCA reduces feature dimension from 142 to 68 resulting in a slight reduction in accuracy to 98.80%.

Keywords: feature extraction; Bengali; handwritten numerals recognition; artificial neural network; machine learning; OCR.

DOI: 10.1504/IJCAT.2020.107424

International Journal of Computer Applications in Technology, 2020 Vol.62 No.4, pp.375 - 383

Received: 12 Nov 2018
Accepted: 18 Sep 2019
Published online: 28 May 2020 *

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