omar-al-sharif / sign-language-recognition

A machine learning project that leverages image processing to recognize and interpret hand gestures

📋 Project Overview

• 👋 Welcome to the Hand Gesture Recognition System Project!

• 🤖 The goal of this project is to implement a system that can recognize hand gestures and classify them into one of six digits (0-5).

• 💡 To achieve this, we implemented a complete machine learning pipeline with modules of preprocessing, feature extraction/selection, model selection and training, and performance analysis.

• 📚 In order to improve the accuracy of our results, we researched the topic extensively and read research papers related to hand gesture recognition.

• 💻 We used the Hand Gesture of the Colombian sign language dataset, which can be downloaded from the link provided.

• 👥 For this project, we only considered the digit classes (0-5) for both men and women datasets.

• 📊 To evaluate our models, we divided the dataset into training, validation, and test sets.

• 🧐 While we are free to use any approach or technique we find appropriate for the problem, we used classical machine learning methods such as support vector machines, KNNs, and random forest.

• 🔍 Let's get started and see what combination of techniques will yield the best results!

💻 Usage

1. Install the required libraries by running

pip install -r requirements.txt

2. Open project.ipynb and run Discussion BLock