Paper Title
SMART RECOGNITION AND INTERPRETATION SYSTEM OF SIGNS AND GESTURES USING MACHINE LEARNING
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Registration ID: IJNRD_324434
Published ID: IJNRD2605134
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Keywords
Sign Language Recognition (SLR), Static Gestures, Dynamic Gestures, Computer Vision, Machine Learning, Data Collection, Preprocessing, Background Subtraction Noise Reduction, Hand Landmark Detection, Transformation, Feature Extraction, Edge Detection, Key-Point Analysis, Classification, Neural Networks, Support Vector Machine (SVM), Deep Learning, OpenCV, Media Pipe, Real-Time Recognition, Occlusion, Scalability, Multi-Modal Recognition, Facial Expressions, Lip Movements
Abstract
Deaf and mute individuals primarily communicate using sign language, a visual-based language composed of specific hand gestures and movements. The advancement of computer vision and machine learning has enabled the development of Sign Language Recognition (SLR) systems that translate these gestures into text or speech, bridging the communication gap between the hearing-impaired and the general population Sign gestures are categorized into static gestures, which involve fixed hand positions, and dynamic gestures, which involve continuous movements. While static gesture recognition is relatively straightforward due to the stable nature of hand positioning, dynamic gesture recognition is more complex, requiring motion tracking, temporal analysis, and feature extraction to achieve accurate interpretation. Despite these challenges, both gesture recognition types play a vital role in human-computer interaction and accessibility solutions. This study explores the fundamental steps involved in sign language recognition, beginning with data collection, preprocessing, and transformation, followed by feature extraction and classification. The collected data, typically in the form of images or video sequences, undergoes preprocessing techniques such as background subtraction, noise reduction, and hand landmark detection. Transformation techniques further refine the input, enhancing recognition accuracy. Feature extraction methods, such as edge detection and key-point analysis, help distinguish different gestures, while machine learning algorithms, including Neural Networks, Support Vector Machines (SVM), and Deep Learning-based models, are used to classify and interpret the signs. Modern technologies such as OpenCV, Media Pipe, and artificial intelligence based recognition models have significantly improved the accuracy and efficiency of sign language translation. However, challenges remain in real-time recognition, variation in sign styles across regions, occlusion, and scalability of systems. Future research should focus on multi-modal recognition, integrating hand gestures, facial expressions, and lip movements to enhance recognition precision.
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How To Cite (APA)
Dandu Sabarish, Darapaneni Durga prasad, Chinthala Pavankumar Reddy, Mr. P. Velayutham Pavanasam, & GALIVEETI GOUTHAM REDDY (May-2026). SMART RECOGNITION AND INTERPRETATION SYSTEM OF SIGNS AND GESTURES USING MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), b252-b259. https://ijnrd.org/papers/IJNRD2605134.pdf
Issue
Volume 11 Issue 5, May-2026
Pages : b252-b259
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Paper Reg. ID: IJNRD_324434
Published Paper Id: IJNRD2605134
Research Area: Other area not in list
Author Type: Indian Author
Country: Chennai, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2605134.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2605134
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