mimounouhd/LeNet5-Tifinagh-AMHCD

LeNet-5 for AMHCD is a classical CNN implementation adapted for Amazigh character recognition. It uses TensorFlow/Keras to process the AMHCD (RGB) dataset, demonstrating the flexibility of the architecture to handle Tifinagh characters. This project serves as an example of applying foundational models to new visual domains. # LeNet5-Tifinagh-AMHCD LeNet-5 for AMHCD is a classical CNN implementation adapted for Amazigh character recognition. It uses TensorFlow/Keras to process the AMHCD (RGB) dataset, demonstrating the flexibility of the architecture to handle Tifinagh characters. This project serves as an example of applying foundational models to new visual domains. Project Title LeNet-5 for AMHCD: A Tifinagh Character Recognition Project Project Overview This project is a deep dive into the classic LeNet-5 convolutional neural network, a foundational architecture that laid the groundwork for modern computer vision. Instead of its traditional role in recognizing simple digits, this implementation boldly adapts it to a more complex and culturally rich domain: the recognition of handwritten Tifinagh characters from the Amazigh Character Handwritten Dataset (AMHCD). This endeavor isn't just about building a model; it's about showcasing the enduring power and flexibility of a seminal architecture when applied to a new, challenging visual language. Why this Project is Extraordinary A Classic Reimagined: This project breathes new life into a classic model. By reprogramming LeNet-5 for a task it wasn't originally designed for, we demonstrate the core principles of deep learning adaptation and its potential beyond a single, specific use case. It's a testament to the idea that great ideas transcend their initial context. Cultural Intersection: We bridge the gap between cutting-edge technology and cultural heritage. By tackling the Tifinagh alphabet, a script with a unique history and visual form, this project serves as a compelling example of how machine learning can be used to preserve and engage with diverse linguistic and cultural traditions. Learning by Doing: This repository is more than just code; it's a comprehensive educational resource. The Jupyter notebook guides you step-by-step, from data loading and preprocessing to model training and evaluation. It's a hands-on journey that …