A Novel Method for the Recognition of Isolated Handwritten Arabic Characters

TL;DR

This paper introduces new preprocessing and feature extraction methods for handwritten Arabic character recognition, achieving high accuracy with neural networks and outperforming existing approaches.

Contribution

It presents novel preprocessing techniques and a comprehensive feature set for improved recognition of handwritten Arabic characters.

Findings

Achieved 88% recognition accuracy on test set.

Outperformed existing methods in accuracy.

Validated effectiveness of combined feature extraction approach.

Abstract

There are many difficulties facing a handwritten Arabic recognition system such as unlimited variation in human handwriting, similarities of distinct character shapes, interconnections of neighbouring characters and their position in the word. The typical Optical Character Recognition (OCR) systems are based mainly on three stages, preprocessing, features extraction and recognition. This paper proposes new methods for handwritten Arabic character recognition which is based on novel preprocessing operations including different kinds of noise removal also different kind of features like structural, Statistical and Morphological features from the main body of the character and also from the secondary components. Evaluation of the accuracy of the selected features is made. The system was trained and tested by back propagation neural network with CENPRMI dataset. The proposed algorithm obtained promising results as it is able to recognize 88% of our test set accurately. In Comparable with other related works we find that our result is the highest among other published works.