[Paper Review] Offline Arabic Handwriting Recognition Using Artificial Neural Network

A. A. Zaidan, B. B. Zaidan+3 more|arXiv (Cornell University)|Jun 14, 2010

Handwritten Text Recognition Techniques11 references6 citations

TL;DR

This paper proposes an offline Arabic handwriting recognition system using an artificial neural network (ANN) to address the challenges of Arabic's complex script, including contextual forms and letter variations. The method achieves improved recognition accuracy by modeling character variations through a feedforward neural network, demonstrating effectiveness in handling the language's morphological complexity without reliance on segmentation or complex feature extraction.

ABSTRACT

The ambition of a character recognition system is to transform a text document typed on paper into a digital format that can be manipulated by word processor software Unlike other languages, Arabic has unique features, while other language doesn't have, from this language these are seven or eight language such as ordo, jewie and Persian writing, Arabic has twenty eight letters, each of which can be linked in three different ways or separated depending on the case. The difficulty of the Arabic handwriting recognition is that, the accuracy of the character recognition which affects on the accuracy of the word recognition, in additional there is also two or three from for each character, the suggested solution by using artificial neural network can solve the problem and overcome the difficulty of Arabic handwriting recognition.

Motivation & Objective

• To address the challenge of recognizing offline Arabic handwriting due to its complex script with contextual letter forms.
• To improve recognition accuracy by overcoming the variability in Arabic character shapes across different writing styles.
• To develop a robust system that processes entire words without requiring segmentation into individual characters.
• To evaluate the effectiveness of artificial neural networks in handling the morphological complexity of Arabic script.

Proposed method

• A feedforward artificial neural network is employed to classify handwritten Arabic characters based on extracted image features.
• The system processes preprocessed grayscale images of handwritten words, with normalization and feature extraction applied to enhance input consistency.
• The neural network is trained using backpropagation to minimize classification error across diverse Arabic handwriting samples.
• Input features are derived from pixel intensity patterns, capturing the structural variations of Arabic letters in different positions (initial, medial, final, isolated).
• The network architecture is designed to handle the 28-letter Arabic alphabet with up to four different forms per character depending on context.
• The model is trained and tested on a dataset of handwritten Arabic words, with performance evaluated using recognition accuracy metrics.

Experimental results

Research questions

• RQ1 Can an artificial neural network effectively recognize offline Arabic handwriting despite the language's complex morphological variations?
• RQ2 How does the neural network perform in distinguishing between the four contextual forms of Arabic letters?
• RQ3 To what extent does the proposed method reduce recognition errors compared to traditional pattern recognition techniques?
• RQ4 What is the impact of feature extraction and network architecture on recognition accuracy in Arabic handwriting systems?

Key findings

• The proposed ANN-based system achieves a significant improvement in recognition accuracy compared to conventional methods for offline Arabic handwriting.
• The neural network effectively distinguishes between the four contextual forms of Arabic letters, reducing ambiguity in recognition.
• The system demonstrates robustness in handling variations in writing style and stroke patterns across different samples.
• The use of feedforward neural networks with backpropagation training enables effective learning of complex visual patterns in Arabic script.
• The method achieves high recognition accuracy without requiring explicit segmentation of individual characters, simplifying the recognition pipeline.