Sign Language Detector Model

Project Overview

This repository contains the implementation of a Sign Language Detector Model using TensorFlow and Keras. The model is designed to recognize and classify different sign language gestures from images, making it a valuable tool for facilitating communication with the deaf and hard-of-hearing community.

Features

• Image Classification: Classifies sign language gestures into 27 different classes, including all 26 letters of the English alphabet and a 'Blank' gesture.
• Deep Learning Model: Utilizes Convolutional Neural Networks (CNNs) for feature extraction and classification.
• Data Augmentation: Implements ImageDataGenerator for image preprocessing and augmentation to improve model robustness.

Prerequisites

• Python 3.x
• TensorFlow 2.x
• Keras
• NumPy
• Pandas
• Matplotlib
• Seaborn

Installation and Setup

1. Clone the repository to your local machine.
2. Install the required Python packages:

   pip install numpy pandas matplotlib seaborn tensorflow keras

3. Download the dataset for Sign Language (links can be provided in the dataset section).

Dataset

The model is trained and validated on a dataset containing images of various sign language gestures. The dataset should be organized into three folders: Train_Alphabet, Test_Alphabet, and Validation_Alphabet.

Model Training

1. The model architecture includes multiple Conv2D and MaxPooling2D layers, followed by Flatten, Dense, and Dropout layers.
2. The model is compiled with Adam optimizer and categorical crossentropy loss function.
3. Train the model using the fit method on the training and validation data.

Usage

1. Load the model using Keras.
2. Preprocess the input image to the required format.
3. Use the model to predict the sign language gesture.

TODO

Complete setting up the web app for the model.

Results and Evaluation

Acknowledgements

Special thanks to all the contributors and researchers in the field of sign language recognition for their valuable insights and datasets.