Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron

TL;DR

This paper presents a method for recognizing handwritten Arabic numerals using an 88-feature set and a Multi Layer Perceptron classifier, achieving nearly 95% accuracy on a 3000-sample dataset.

Contribution

It introduces a novel feature set and applies an MLP classifier specifically for handwritten Arabic numeral recognition, demonstrating high accuracy.

Findings

Achieved 94.93% average recognition rate.

Developed a comprehensive feature set with 88 features.

Validated results with three-fold cross validation.

Abstract

Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence. Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters. Considering all these, the problem of handwritten numeral recognition is addressed under the present work in respect to handwritten Arabic numerals. Arabic is spoken throughout the Arab World and the fifth most popular language in the world slightly before Portuguese and Bengali. For the present work, we have developed a feature set of 88 features is designed to represent samples of handwritten Arabic numerals for this work. It includes 72 shadow and 16 octant features. A Multi Layer Perceptron (MLP) based classifier is used here for recognition handwritten Arabic digits represented with the said feature set. On experimentation with a database of 3000 samples, the technique yields an average recognition rate of 94.93% evaluated after three-fold cross validation of results. It is useful for applications related to OCR of handwritten Arabic Digit and can also be extended to include OCR of handwritten characters of Arabic alphabet.