SRCantona/Arabic_letters

Arabic Letters Dataset

Introduction

This repository contains a comprehensive dataset focused on Arabic letters, curated from multiple collectors to support research and development in handwriting recognition, machine learning, and related fields.

About Arabic Letters

The Arabic alphabet is a beautiful and intricate writing system, consisting of 28 letters. Unlike Latin scripts, Arabic is written from right to left, and its letters can change shape depending on their position in a word:

• Initial: When a letter appears at the beginning of a word, it has a specific form.
• Medial: In the middle of a word, letters often connect to both the preceding and following letters, adapting their shape.
• Final: At the end of a word, letters have a distinct, sometimes more elaborate form.
• Isolated: When a letter stands alone, it takes on its independent shape.

Arabic letters do not include uppercase and lowercase distinctions, but the connection style makes handwriting uniquely dynamic. This feature adds both beauty and complexity to handwriting recognition tasks.

Dataset Description

The dataset is divided into three files, collected by different contributors:

Hijja2-master

The Hijja2-master dataset contains Arabic handwritten characters collected from a variety of individuals, offering a diverse range of handwriting styles. It provides structured data suitable for character recognition tasks and includes images of individual Arabic letters in different forms. This dataset is beneficial for developing robust models capable of handling various handwriting styles.

HMBD-v1-master

The HMBD-v1-master dataset consists of 54,115 images of Arabic handwritten characters. It captures a wide range of handwriting styles from multiple contributors, making it a comprehensive resource for training and evaluating handwriting recognition models. The dataset supports tasks such as character classification and pattern recognition.

Mohamed Loey Dataset

Curated by Mohamed Loey, this dataset contains Arabic handwritten characters labeled with their corresponding letters. The dataset is divided into two subsets: one for training images and the other for testing images. This division helps in developing and evaluating machine learning models efficiently, ensuring robust performance across different handwriting styles.

Usage

These datasets can be used for:

• Handwriting Recognition: Training models to identify Arabic letters from handwritten inputs.
• Machine Learning Projects: Suitable for supervised learning tasks like classification and clustering.
• Pattern Analysis: Studying the variations in Arabic handwriting across different individuals.

How It Works

The datasets typically include images of handwritten Arabic letters with corresponding labels. Here’s a basic workflow:

1. Data Preprocessing: Convert images to grayscale, resize them, and normalize pixel values.
2. Model Training: Use CNNs or other machine learning algorithms for classification tasks.
3. Evaluation: Test the model on unseen data to evaluate its performance and accuracy.