sign-language-recognition-ai-project

AI-based sign language recognition using deep learning, research gap analysis, and optimized search techniques.

Sign Language Recognition AI Project

Overview

This project focuses on AI-based sign language recognition using deep learning techniques. The goal is to recognize hand gestures accurately and improve existing approaches by addressing research gaps such as lack of AI search frameworks, random search paths, missing heuristic-guided search, no transfer learning, and no temporal search.

Problem Statement

Sign language recognition systems help bridge communication gaps for the deaf and hard-of-hearing community. However, many existing models are limited by static datasets, inefficient optimization methods, and weak temporal understanding. This project aims to build a better recognition system using AI and modern deep learning methods.

Objectives

• Study recent research on sign language recognition.
• Identify limitations in existing models.
• Build a deep learning-based recognition model.
• Improve performance using optimized search or heuristic techniques.
• Explore scalable and real-time capable solutions.

Dataset

This project can use the ASL Alphabet Dataset from Kaggle:

• Dataset Link: ASL Alphabet Dataset
• The dataset contains about 87,000 images of ASL alphabet gestures with 29 classes, including A-Z plus SPACE, DELETE, and NOTHING.

Alternative dataset:

• Sign Language MNIST, which is a grayscale hand gesture dataset modeled after MNIST and commonly used for CNN-based classification.

Technologies Used

• Python
• NumPy
• Pandas
• OpenCV
• TensorFlow / Keras or PyTorch
• MediaPipe (optional for hand landmark detection)
• Matplotlib / Seaborn

Methodology

1. Collect or download the dataset.
2. Preprocess images or video frames.
3. Extract useful gesture features.
4. Train a deep learning model such as CNN, CNN+RNN, LSTM, or ResNet-based architecture.
5. Evaluate model performance using accuracy, precision, recall, and F1-score.
6. Improve the system using optimized search strategies or heuristic guidance.

Research Gaps Addressed

Based on reviewed literature, this project is designed to address the following gaps:

• No AI search framework.
• Random search paths.
• No heuristic-guided search.
• No transfer learning.
• No temporal search.

Expected Outcome

• Accurate sign language gesture recognition.
• Better performance than basic baseline models.
• Improved real-time applicability.
• A foundation for future deployment in assistive communication systems.

Installation

git clone https://github.com/your-username/sign-language-recognition-ai-project.git
cd sign-language-recognition-ai-project
pip install -r requirements.txt

Usage

python main.py

Future Enhancements

• Real-time webcam-based sign recognition.
• Support for dynamic gestures and sentence-level recognition.
• Transfer learning for improved accuracy.
• MediaPipe-based hand tracking integration.
• Web or mobile deployment.

Author

Ankith R MCA in AI and Data Science
Amrita Vishwa Vidyapeetham, Mysuru