CNN-Based Hand Gesture Recognition System

Sign language is a system of communication using Visual gestures and signs.Hearing impaired people and the deaf and dumb community use sign language as their only means of communication. Understanding sign language is so much difficult for a normal person. Therefore, the minority group has always faced many difficulties in communicating with the General population. In this research paper, we proposed a new deep learning-based approach to detect sign language, which canremove the barrier of communication between normal and deaf People. To detect real-time sign language first we prepared a Dataset that contains 11 sign words and 26 Alphabets. We used these sign words to Train our customized CNN model. We did some preprocessing in The dataset before the training of the CNN model.. The field of sign language detection has witnessed significant advancements driven by the integration of computer vision, machine learning, and natural languageprocessing techniques. This research focuses on the development of a robust and adaptive system for interpreting sign language gestures, facilitating effective communication between individuals who use sign language and those who may not be familiar with it. Leveraging computer vision, the system captures and analyzes video or image data to recognize intricate hand movements, facial expressions, and other relevant features associated with sign language. Machine learning algorithms, particularly convolutional neural networks (CNNs), are employed to train models that ensure accurate and real-time gesture recognition. Wearable devices, such as gloves or wrist-worn sensors, equipped with motion sensors, offer an alternative approach for capturing and interpreting hand and body movements. The integration of natural language processing techniques further enhances the system's ability to interpret the linguistic aspects of sign language.