ASL-Alphabet-Recognition

ASL Alphabet Detection is a machine learning-based project designed to recognize and classify American Sign Language (ASL) alphabets from hand gesture images. Built in Python using Jupyter Notebook, the project trains a model to predict ASL alphabets with high accuracy and includes tools for data visualization and evaluation.

View Repository on GitHub ↗

REPOSITORY OVERVIEW

Live repository statistics

★ 0Stars

⑂ 0Forks

◯ 0Open issues

◉ 0Watchers

34/100

OPENREPOHUB HEALTH SIGNAL

Limited signals

A transparent discovery signal based on current public GitHub metadata.

Recent activity35% weight

Community adoption25% weight

Maintenance state20% weight

100

License clarity10% weight

Project information10% weight

This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.

README preview

ASL Alphabet Detection

This project is a machine learning-based solution for recognizing and detecting American Sign Language (ASL) alphabets. The model processes input images of hand gestures and predicts the corresponding ASL alphabet. The implementation is carried out in a Jupyter Notebook and leverages popular machine learning frameworks for model development and training.

Features

ASL Alphabet Detection:
- Recognizes hand gestures and maps them to ASL alphabets.
- Designed for real-time or static image input.
Model Training:
- Uses supervised learning to train on a dataset of ASL alphabet images.
Evaluation:
- Tests model accuracy and performance on unseen data.
Visualization:
- Includes visualization of input data, training process, and predictions.

Technologies Used

Python: Core programming language.
Jupyter Notebook: For interactive code and experimentation.
Machine Learning Frameworks:
- PyTorch
- OpenCV for image processing.
Libraries: NumPy, Pandas, Matplotlib, Scikit-learn.