Conversion Of Sign Language into Text and Speech Using CNN
Keywords:
Sign Language Recognition, Text Conversion, Convolutional Neural Networks (CNN), Deep Learning, Gesture Recognition, Image Processing, Real-Time Processing, Assistive Technology, Human-Computer Interaction, Feature Extraction, Neural Networks, Machine LearningAbstract
This research paper presents a comprehensive study on the design and implementation of a sign language to text conversion system powered by Convolutional Neural Networks (CNNs). Our work addresses the critical communication barriers faced by the deaf and hard-of-hearing communities by developing an automated framework that accurately recognizes sign language gestures from image and video inputs and converts them into corresponding textual output. The system leverages state-of-the-art deep learning techniques alongside robust image processing algorithms, ensuring real-time performance and high recognition accuracy. By integrating advanced feature extraction methods, efficient data pre-processing, and sophisticated model architectures, our approach demonstrates promising potential for practical applications in assistive technologies, education, and healthcare.
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