Implementation on Sign Language Recognition
Mr.Mandar Gawande
Mr.Shubham Haral
Ms.Pooja Maknor
Ms.Vaishnavi Nerpgar
Matoshri College of Engineering and Research Center Nashik.
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Abstract: Sign language is a vital form of communication for the deaf and hard-of-hearing community, playing a crucial role in fostering social interaction, education, and access to essential services. However, a significant barrier exists between sign language users and non-signers, often leading to misunderstandings and reduced accessibility in various contexts. To address this challenge, we propose a novel real-time sign language detection system that utilizes standard web cameras, aiming to bridge the communication gap effectively.
This innovative system is designed to recognize sign language gestures performed in front of the camera and convert them into both voice output and onscreen text. By leveraging advanced computer vision and deep learning techniques, the system captures and analyzes both hand movements and facial expressions, which are critical for conveying meaning in sign language. It identifies key markers corresponding to specific signs through the application of sophisticated algorithms, ensuring accurate gesture recognition.
Once these markers are detected, machine learning algorithms translate them into a comprehensive sign language vocabulary. This process allows for nuanced understanding and interpretation of the signs being used. The system provides dual output: voice for real- time interpretation, facilitating immediate communication for those who may not be familiar with sign language, and on-screen text, which serves as a visual reference for users.
The dual-output feature of the system significantly enhances accessibility and inclusivity, empowering a broader audience to engage in meaningful conversations with sign language users. This technology not only supports individuals in personal
Interactions but also has the potential for integration into webcams and other devices equipped with cameras, making it widely applicable.
Key Words: Sign Language Recognition, Real-Time Detection, Computer Vision, Deep Learning,
Gesture
Recognition, Accessibility, Dual Output Voice Output, On-Screen Text
