🖐️ Sign Language Detection

This project implements a real-time sign language detection system using TensorFlow and OpenCV.
The goal is to bridge the communication gap between the hearing-impaired and others by translating sign language gestures into text or speech.

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Features

• Real-time sign detection using webcam.
• Deep learning models (TensorFlow Object Detection API).
• 🗂 Custom dataset support for training your own gestures.
• 🖼 Image preprocessing & augmentation pipeline.
• Evaluation metrics and visualization.
• Easy-to-run scripts for training and inference.

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📂 Project Structure

SignLanguageDetection/ │── Tensorflow/ │ ├── workspace/ │ │ ├── annotations/ # Label files (.xml/.csv) │ │ ├── images/ # Train/Test images │ │ ├── models/ # Trained models and checkpoints │ │ └── pre-trained-models/ # Downloaded pre-trained TF models │ └── scripts/ # Utility scripts (training, evaluation, etc.) │── README.md # Project documentation

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⚙️ Installation

1. Clone the repository

   git clone https://github.com/anjaliy11/SignLanguageDetection.git
   cd SignLanguageDetection
   
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Create a virtual environment

conda create -n signlang python=3.10 -y conda activate signlang

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Install dependencies

Install TensorFlow Object Detection API git clone https://github.com/tensorflow/models Tensorflow/models cd Tensorflow/models/research protoc object_detection/protos/*.proto --python_out=. pip install . cd ../../..

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▶️ Usage

1. Collect Images

Use your webcam to capture images for different gestures: python Tensorflow/scripts/collect_images.py

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2. Label Images

Label the collected images using LabelImg.

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3. Train Model

python Tensorflow/scripts/train.py

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4. Real-time Detection

Run the detection script: python Tensorflow/scripts/detect.py

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Tech Stack

Python

TensorFlow

Keras

OpenCV

NumPy

Pandas

Matplotlib

LabelImg (for annotation)