Real-Time Hand Sign Recognition System

Abstract

The "Real-Time Hand Sign Recognition System" is designed to address the communication challenges faced by individuals with hearing impairments. The system leverages convolutional neural networks (CNNs) for real-time recognition of hand signs, translating them into textual or spoken language to facilitate communication between sign language users and others. Using advanced technologies such as MediaPipe and OpenCV for hand detection and video processing, the system ensures accurate, low-latency recognition under various lighting and environmental conditions.

Specification

Description:FIELD OF INVENTION
[001] This invention pertains to the field of assistive communication technologies, specifically focused on the real-time detection and recognition of hand gestures for sign language interpretation. It applies deep learning and computer vision techniques to facilitate enhanced interaction for individuals with speech and hearing impairments.
BACKGROUND AND PRIOR ART
[002] Existing systems for hand gesture recognition have limitations, including low accuracy, insufficient real-time processing, and lack of adaptability to diverse environments. Prior art includes various approaches using deep learning, sensor-based recognition, and hybrid systems. These have demonstrated the potential for gesture recognition but fall short in offering a comprehensive, real-time solution for dynamic hand sign detection across multiple sign languages.
SUMMARY OF THE INVENTION
[003] The invention offers a sophisticated real-time hand sign recognition system that bridges the communication gap for individuals with hearing impairments. The system uses CNNs trained on a custom dataset to recognize static hand signs for the alphabet, translating them into text and speech. The combination of MediaPipe for precise hand detection and OpenCV for video processing ensures real-time performance with high accuracy. The system also provides dynamic subtitle generation and text-to-speech synthesis for inclusive communication in diverse settings.
BRIEF DESCRIPTION OF DRWAINGS
Figure 1 shows Architecture of Real-Time Hand Sign Recognition System
DETAILED DESCRIPTION OF THE INVENTION
[004] The invention employs a convolutional neural network-based architecture for sign language recognition, which captures hand gestures via a standard webcam and processes the input in real-time. The video is converted into frames, and the hand region is segmented using MediaPipe. These segments are analyzed to detect specific hand shapes, which are then classified using a pre-trained CNN model. The system outputs the recognized hand sign as text and also provides an audible translation using text-to-speech software. The model is designed for scalability and can be trained on different sign languages, making it adaptable for future development , C , Claims:[005] 1. A real-time hand sign recognition system that captures and processes hand gestures using convolutional neural networks.
[006] 2. The system leverages MediaPipe for accurate hand detection and OpenCV for real- time video processing.
[007] 3. The invention translates hand signs into text and speech, facilitating communication for individuals with speech and hearing impairments.
[008] 4. The system is adaptable for use with various sign languages and is scalable for future improvements, including dynamic gesture recognition and integration with mobile platforms and wearable devices.
[009] 5. The invention achieves high accuracy under varying lighting conditions and environments, making it suitable for real-world communication applications.

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