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ABSTRACT
We provide abstract of cnn machine learning in this paper
Introduction: This research explores the utilization of Convolutional Neural Networks (CNN) for the conversion of sign language gestures into either spoken words or text. Sign language serves as a primary means of communication for individuals with hearing impairments, and this study aims to bridge the communication gap by developing an efficient system that translates sign language gestures into accessible formats.
Methodology: The research employs a CNN-based approach for the recognition and interpretation of sign language gestures.
Recognition and Classification: The CNN model is designed to recognize and classify different sign language gestures accurately. Through the use of deep learning techniques, the model learns to extract meaningful features from input images, enabling it to distinguish between various signs with a high level of accuracy.
Conversion to Speech: Upon successful recognition of sign language gestures, the system proceeds to convert the interpreted signs into spoken words. This seamless conversion enhances the accessibility of information for both deaf and hearing individuals.
Conclusion: The proposed CNN-based system proves to be effective in converting sign language into either speech or text, contributing significantly to inclusive communication for individuals with hearing impairments. The integration of deep learning techniques ensures robust recognition and interpretation, while the flexibility in output options enhances the adaptability of the system in diverse communication scenarios.
