Project Title:Sign Language Recognition Using Machine Learning

Objective:

To develop a machine learning model that can recognize and translate sign language gestures into text or speech.

Summary:

This project aims to create a system capable of recognizing sign language gestures and translating them into text or speech. It involves training a machine learning model using a dataset of sign language gestures, which could be represented in the form of images, video frames, or hand movements captured via sensors. The goal is to help bridge communication gaps between individuals who are deaf or hard of hearing and the broader community.

Typically, the project involves using computer vision techniques to recognize hand shapes, positions, and movements, or sensor data from gloves or wearable devices to classify signs. Techniques like Convolutional Neural Networks (CNNs) for image-based recognition or Recurrent Neural Networks (RNNs) for sequence-based data are often used.

Key Steps:

Collect Data – Use sign language datasets (e.g., American Sign Language (ASL) dataset, RWTH-PHOENIX-Weather dataset) or create a custom dataset of sign gestures.

Data Preprocessing – Clean and prepare the data, such as resizing images, normalizing, or converting videos to frames. If using sensors, preprocess sensor data.

Model Training – Train a model using deep learning algorithms like CNNs (for image recognition) or RNNs/LSTMs (for sequential data).

Model Evaluation – Test the model using accuracy, confusion matrix, or real-time prediction performance.

Technologies Used:

Python

TensorFlow / Keras / PyTorch (for deep learning models)

OpenCV (for image/video processing)

Scikit-learn (for traditional machine learning models)

Matplotlib / Seaborn (for visualizing results)

Applications:

Assistive communication tools for deaf or hard of hearing individuals.

Mobile apps that translate sign language into text or speech in real-time.

Human-computer interaction systems, allowing for hands-free control of devices.

Education for learning and practicing sign language.

Expected Outcomes:

A trained model that can recognize and translate sign language gestures into text or speech.

Performance evaluation metrics such as accuracy, precision, and recall.

A demonstration of real-time sign language recognition (optional, using a webcam or wearable sensors).