The aim of this project is to develop a Sign Language IoT device using Python, Raspberry Pi, ESP32-CAM, and an LCD display that can accurately detect sign language gestures as alphabets and construct coherent sentences in real-time.
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Hardware: Raspberry Pi: The central processing unit that hosts the algorithm and manages the overall functionality. ESP32-CAM module: Responsible for capturing high-resolution images of sign language gestures. LCD display: Provides immediate visual feedback of the interpreted sign language sentences.
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Software and Programming Tools: Python: The primary programming language for developing algorithms and controlling the device. OpenCV and TensorFlow: Libraries for computer vision and machine learning, facilitating accurate gesture recognition. Development Environment: Integrated Development Environment (IDE) for Python programming, such as Thonny or PyCharm.
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Algorithm Development and Training: Datasets: Diverse sign language datasets for training the machine learning models. Training Platform: Access to computational resources for training and optimizing machine learning models.
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Wireless Communication: Wireless Communication Module: Components and protocols (e.g., Wi-Fi, Bluetooth) for enabling remote interaction.
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Testing and Validation: Testing Environment: Set up for validating the accuracy and responsiveness of the developed system. User Feedback: Involvement of individuals proficient in sign language for real-world testing and feedback.
This research introduces an innovative Sign Language Internet of Things (IoT) device, implemented using Python programming language, Raspberry Pi, ESP32-CAM, and an LCD display, to empower individuals with hearing impairments through enhanced communication. The device leverages computer vision and machine learning techniques to detect sign language gestures as alphabets, seamlessly constructing meaningful sentences in real-time. The system employs a Raspberry Pi as the central processing unit, integrating an ESP32-CAM module for capturing high-resolution images of sign language gestures. A Python-based algorithm, utilizing OpenCV and libraries, interprets hand movements and facial expressions, accurately recognizing individual alphabets with a high degree of precision. The lightweight nature of the Python language facilitates efficient on-device processing, ensuring low latency and real-time responsiveness.
Despite the increasing integration of technology in various aspects of our lives, individuals with hearing impairments continue to face communication barriers that hinder their full participation in social, educational, and professional settings. Traditional methods of communication often fall short in meeting the needs of the deaf and hard-of-hearing community, with sign language being a primary means of expression. However, the lack of widespread understanding of sign language poses a significant challenge, leading to miscommunication and isolation for individuals who rely on this visual language. This research seeks to contribute to the resolution of these challenges by developing a practical, low-cost, and versatile Sign Language IoT device that can serve as a valuable tool in breaking down communication barriers and promoting inclusivity for individuals with hearing impairments.
Aim: The aim of this research is to develop a Sign Language IoT device using Python, Raspberry Pi, ESP32-CAM, and an LCD display that can accurately detect sign language gestures as alphabets and construct coherent sentences in real-time. The primary focus is to create an affordable, portable, and user-friendly solution to empower individuals with hearing impairments in their communication endeavours.
Objectives:
- To provide an IoT device that can detect Sign Languages in Real-Time.
- To enable Sign Gestures into Text.
- To ensure portability and User-friendly interface
- To enhance the accuracy of the model by optimizing machine learning algorithm.