Sign – A Sign Language to Text Conversion System

Sign - A Sign Language to Text Conversion System

Authors:
Raihan Ashraf (raihanashraf30@gmail.com)

Ms. Nikita Rawat Assistance Professor (niks.rawat23@gmail.com)

Shri Rawatpura Sarkar University

Abstract

Communication is the foundation of human interaction, yet for millions of hearing and speech-impaired individuals, the inability to communicate through spoken language creates a significant social and emotional barrier. Sign language is their primary means of expression, but since the majority of people are not trained to understand it, meaningful communication often becomes limited. To address this challenge, this project — “Sign Language to Text Language Conversion System” — has been developed to translate hand gestures of sign language into readable text, thereby bridging the gap between the hearing-impaired community and the rest of society.

The system employs computer vision and machine learning algorithms to detect, interpret, and translate hand gestures into text in real-time. Using a live camera feed or image input, the system captures the user’s hand movements and identifies the specific sign being displayed. The input image is pre-processed using Digital Image Processing (DIP) techniques such as background subtraction, skin color segmentation, and contour detection to isolate the hand region. Then, a trained deep learning model—based on Convolutional Neural Networks (CNNs)—analyzes the gesture and maps it to the corresponding alphabet, word, or phrase. The recognized output is displayed as readable text on the user interface.

This project integrates both software and hardware components, where the software handles gesture recognition and the hardware (such as webcam or embedded sensors) captures the sign input. The system can be deployed on computers, smartphones, or IoT-enabled devices, providing flexibility and accessibility. It is designed with an emphasis on accuracy, speed, and usability, ensuring that it works effectively under varying lighting conditions, hand orientations, and skin tones.

The proposed model can be extended further by incorporating Natural Language Processing (NLP) techniques to convert the recognized signs into grammatically correct sentences and adding Text-to-Speech (TTS) functionality to generate voice output. This enhancement will enable bidirectional communication—where spoken responses can also be converted back into sign gestures for complete interaction. Future advancements may include the integration of 3D sensors, wearable devices, and AI-driven gesture prediction models to support regional sign language variations and enhance precision.

1.1   Keywords

The main keywords of this project are Sign Language Recognition, Gesture Detection, Digital Image Processing (DIP), Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Computer Vision, Artificial Intelligence (AI), Text Conversion, and Assistive Technology. These technologies work together to detect and interpret sign gestures, converting them into readable text to enhance real-time communication between hearing and speech- impaired individuals and the general public.