Hand Sign Detection

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Aman khan; Mohd zaman; Waseem khan; Khadeeja Haneef; Mohd Aqib

Hand Sign Detection

Authors: Aman khan, Mohd zaman, Waseem khan, Khadeeja Haneef, Mohd Aqib

Unique Paper ID: 174040
Volume: 11
Issue: 10
PageNo: 4529-4533

Keywords: No Keywords Found

Abstract:
Hand sign detection is an emerging AI-driven field aimed at improving communication for individuals with hearing and voice impairments. This research introduces a deep learning-based system that recognizes hand gestures, changed them into text, and voice output. The system utilizes OpenCV for image processing, Mediapipe for hand tracking, and TensorFlow/Keras to train a CNN for gesture classification. Recognized gestures are mapped to text and converted into speech using the pyttsx3 library. A dataset of labeled hand images was collected and preprocessed using augmentation techniques to enhance model accuracy. The trained CNN achieved approximately 90% accuracy in classification. However, challenges such as lighting variations, hand size differences, and complex backgrounds affect recognition performance. The system currently supports a limited set of gestures and does not handle dynamic sign language. Future improvements will focus on expanding gesture recognition, refining real-time processing, and integrating NLP for full sign language interpretation. Deployment on mobile and embedded devices is also planned for broader accessibility.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 10
PageNo: 4529-4533

Hand Sign Detection

Available:https://ijirt.org/article?manuscript=174040

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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