American Sign Language Detection Using Machine Learning

Abstract

This project introduces a system that helps deaf and mute individuals interact more easily by converting sign language into spoken or written words. It uses image processing and a deep learning model called Convolutional Neural Network (CNN) to identify hand gestures. The system analyzes each gesture through multiple layers to extract features and classify them accurately. This approach can improve communication in everyday situations and medical environments, and it can also be expanded into a mobile application for quick and wireless communication support. The human hand, being highly expressive, is frequently used not only for physical interaction but also for communication. For deaf and mute individuals, hand gestures form the foundation of sign language, which is essential for daily communication. Enabling computers to understand and interpret these gestures would mark a significant advancement in human-computer interaction. The development of such systems requires effective manipulation and processing of visual data

Introduction

Project Objective

This project develops a real-time gesture recognition system using deep learning, specifically Convolutional Neural Networks (CNNs), to assist individuals with speech or hearing impairments. It aims to convert sign language gestures into text or speech, promoting seamless communication and social inclusion.

Motivation

Despite technological advances, many differently-abled individuals remain excluded from mainstream communication tools. Over 300 million people globally have hearing or speech impairments (WHO). This project proposes a device-free, camera-based, and AI-driven solution to bridge this communication gap.

System Features

• Input: Hand gesture images captured via webcam.

• Processing: CNN-based model extracts spatial features.

• Output: Text or speech form of the recognized gesture.

• Goal: Create a portable, scalable, and mobile-compatible communication tool.

Literature Review Highlights

1. Spatial Feature Extraction: Using centroids and symbolic data to handle sign variations.

2. ASL Recognition Techniques: Comparing methods for static and dynamic gesture recognition.

3. Hybrid Approach: Combining deep learning (e.g., VGG-19) with handcrafted features (e.g., LBP) for better accuracy.

4. Cross-User Evaluation: Moderate success in recognizing gestures from new users.

5. Shape Analysis: MLP-based classification of static hand gestures with high accuracy (86.38%).

Existing System Limitations

• Hardware Dependency (gloves/sensors)

• Limited Vocabulary (mostly static gestures)

• Sensitivity to lighting, background, user differences

• Low Real-Time Performance

• Poor Scalability and generalization

Proposed System Improvements

• Device-Free Operation: No external hardware needed.

• Real-Time Recognition: Faster processing and response.

• Deep Learning (CNN): For accurate gesture classification.

• User-Independent Design: Works across users and conditions.

• High Accuracy: Up to 97% recognition success.

• Multimodal Output: Supports both text and speech.

• Scalable and Expandable: Can grow to include new signs/languages.

System Modules

1. Input Capture: Webcam collects gesture images.

2. Preprocessing & Segmentation: Filters background; isolates hand region.

3. Feature Extraction: Shape, texture, and spatial features stored for classification.

4. Classification: Uses SVM to compare gesture features and determine match.

5. Result Display: Recognized gesture output is shown in real-time.

System Architecture

A modular design ensures efficient data flow from image input to result display. Each module (input, processing, classification, output) is designed for seamless integration, ensuring robustness and real-world usability.

Conclusion

Communication plays a vital role in daily life. However, individuals with hearing and speech impairments face considerable challenges when interacting with others who are not familiar with sign language.Hand gestures and sign language are most commonly used by the deaf and mute community. This project presents a step toward bridging the communication gap between people with such impairments and those unfamiliar with sign language. By using Convolutional Neural Networks (CNNs), the system successfully recognizes hand gestures corresponding to English alphabets with commendable accuracy. The implemented model demonstrates that machine learning, especially deep learning techniques like CNNs, can be effectively used to identify sign language gestures, making communication more inclusive and accessible for everyone. Future Scope: In real-world applications, sign language recognition needs to operate on live video streams rather than static images. Future development of this system can focus on incorporating real-time video processing, where continuous hand movements are captured frame by frame for gesture detection and interpretation.

References

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Copyright

Copyright © 2025 Supriya Singh, Dr. V. Uma Rani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.