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The Arabic Digits Recognition project aims to develop a robust Convolutional Neural Network (CNN) model capable of accurately recognizing spoken Arabic digits. This project leverages a diverse dataset collected from 65 individuals of varying ages and genders, ensuring a comprehensive representation of the Arabic-speaking population. The goal is to facilitate advancements in voice recognition technology and improve accessibility for Arabic speakers.

• Diverse Dataset: Collected from 65 individuals, ensuring representation across different ages and genders.
• High Accuracy: The CNN model achieves impressive accuracy in recognizing Arabic digits.
• User-Friendly Interface: Designed to be easily integrated into applications for digit recognition.
• Visualization Tools: Includes visualizations of training progress and model performance.

• Programming Language: Python
• Framework: TensorFlow and Keras
• Libraries: NumPy, Pandas, Matplotlib, Librosa
• Data Processing: ETL (Extract, Transform, Load) methodology
• Model Architecture: Convolutional Neural Network (CNN)

Before starting 🏁, you need to have the following software installed:

• Git: A free and open-source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

• Python: A powerful programming language that is easy to learn and widely used for various applications, including machine learning and data analysis.

• TensorFlow: An open-source machine learning framework that provides a comprehensive ecosystem for building and deploying machine learning models.

• Librosa: A Python package for music and audio analysis, providing the building blocks necessary for the analysis of audio signals in Python.

Make sure to follow the installation instructions provided on each link to set up the required tools properly.

To get started with the Arabic Digits Recognition project, follow these steps:

• Python 3.8 or higher
• Required libraries (install using pip):

  pip install numpy pandas matplotlib librosa tensorflow keras

1. Data Collection: Collect audio samples of spoken Arabic digits from a diverse group of participants.
2. ETL Process:
   • Extract: Gather audio files and metadata.
   • Transform: Preprocess audio files (e.g., normalization, feature extraction using Mel-frequency cepstral coefficients (MFCCs)).
   • Load: Store the processed data into a format suitable for training (e.g., NumPy arrays).

1. Clone the repository:

   git clone https://github.com/yourusername/arabic-digits-recognition.git
   cd arabic-digits-recognition

2. Run the training and evaluation script:

   python main.py

This project is licensed under the MIT License. See the LICENSE file for more details.

M. N. Gaber
AI & ML.Eng

• Special thanks to all participants who contributed their voice samples.
• Thanks to the open-source community for the libraries and frameworks used in this project.

For questions or feedback, please reach out to abunasseredu@gmail.com.

Feel free to contribute to this project by submitting pull requests or reporting issues. Your contributions are welcome! 🤝