Volume no :
1 |
Issue no :
4
Article Type :
Scholarly Article
Author :
M. Sandhya Rani, C.V.N. Pradeeth,G. Jagadish,K. Ganesh
Published Date :
March, 2025
Publisher :
INTERNATIONAL JOURNAL OF ENGINEERING INNOVATIONS AND MANAGEMENT STRATEGIES
Page No: 1 - 14
Abstract : A Sign Language Translator using OpenCV is an innovative application of computer vision and machine learning technologies aimed at bridging the communication gap between hearing-impaired individuals and the general population. This system primarily utilizes OpenCV, an open-source computer vision library, to detect, interpret, and translate hand gestures corresponding to sign language into readable or audible text in real time. The core objective of this project is to create a cost-effective, non-invasive, and accessible tool that enhances communication for those who rely on sign language. The translator system captures hand gestures using a webcam or camera module, then processes the image frames through a series of operations including background subtraction, color space conversion (usually to HSV or grayscale), contour detection, and segmentation to isolate the hand region. Key features like finger positions, angles, and hand shapes are extracted using image processing techniques, and these features are mapped to predefined gesture classes representing alphabets, words, or phrases of sign language. Machine learning models such as Convolutional Neural Networks (CNNs) can be trained on datasets of hand signs to improve recognition accuracy, enabling the system to handle variations in lighting, background, and hand orientation. Real-time feedback is provided by displaying the translated text on the screen or through speech synthesis using text-to-speech engines, allowing for dynamic and interactive communication. Challenges such as overlapping gestures, rapid hand movement, and skin tone variation are addressed through preprocessing steps and data augmentation techniques during model training. The integration of OpenCV ensures efficient image processing while maintaining low computational overhead, making it feasible for implementation on laptops, smartphones, or embedded systems like Raspberry Pi. Furthermore, the system can be customized to support various sign languages including American Sign Language (ASL), Indian Sign Language (ISL), and others, making it a versatile tool across different regions and cultures. Future enhancements may include the incorporation of deep learning models like LSTM for gesture sequence recognition and the use of depth cameras for improved spatial accuracy. In conclusion, a Sign Language Translator using OpenCV exemplifies the impactful use of technology to foster inclusive communication, reduce social barriers, and promote accessibility for the deaf and hard-of-hearing community through an affordable, real-time, and user-friendly solution.
Keyword Sign Language Recognition, OpenCV, Hand Gesture Detection, Computer Vision, Real-Time Translation, Human-Computer Interaction, Machine Learning Ask ChatGPT
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