sign-language

Project Title: Real-Time Sign Language Detection using YOLO in Jupyter Notebook

Description: This project implements a real-time sign language recognition system using the YOLO (You Only Look Once) object detection algorithm. The model is designed to detect and classify various sign language gestures from images or video streams, enabling smooth and accurate communication between sign language users and non-signers.

The dataset consists of labeled images representing different hand gestures from a sign language alphabet. The YOLO model is trained to recognize these gestures by learning their unique shapes, orientations, and positions in an image.

Using Jupyter Notebook, the project follows a step-by-step approach, including:

Data Preparation – Collecting and annotating sign language images, converting them into YOLO-compatible format.

Model Training – Configuring and training the YOLO model to detect multiple classes of gestures.

Evaluation – Measuring model accuracy, precision, and recall using test data.

Real-Time Detection – Integrating the trained model with a webcam feed to detect signs live.

Key Features:

Built using YOLO for high-speed and accurate gesture detection.

Runs within Jupyter Notebook for easy experimentation and visualization.

Supports real-time recognition via OpenCV integration.

Can be extended to multiple sign languages or larger gesture vocabularies.

Technologies Used:

Python (NumPy, Pandas, OpenCV, Matplotlib)

YOLOv5 / YOLOv8 (for object detection)

LabelImg or Roboflow (for dataset annotation)

Jupyter Notebook (for training, testing, and visualization)

Applications:

Assistive technology for Deaf and hard-of-hearing individuals.

Educational tools for learning sign language.

Integration with chatbots or translation systems for accessibility.