SPARSHNET is a web application designed to assist deaf individuals in understanding spoken language by predicting what is being said through lip movement recognition. Leveraging Deep Learning techniques, SPARSHNET aims to bridge the communication gap for the deaf community, offering a tool that interprets spoken words visually.
Deaf individuals face significant challenges in understanding spoken language due to their inability to perceive auditory cues. While sign language is a vital mode of communication for many, it requires proficiency and may not always be accessible in every social or professional context. Lip reading offers an alternative, but it requires substantial skill and can be challenging due to variations in speech patterns, accents, and obscured views.
Existing lip reading aids often rely on manual input or limited pre-defined vocabulary, limiting their practicality and accuracy. This creates a barrier to effective communication, hindering the inclusion and participation of the deaf community in various aspects of daily life, education, and work.
SPARSHNET addresses these challenges by utilizing Deep Learning algorithms to recognize and interpret lip movements. By analyzing video input of a speaker's face, the application predicts the corresponding spoken words, providing a visual representation of the speech.
Key features of SPARSHNET include:
lip movement recognition: The application processes recorded video feeds, accurately identifying lip movements and translating them into text.
Accessibility: SPARSHNET is accessible via a web browser, making it widely available on various devices without the need for specialized hardware or software.
Customizable vocabulary: The application can be trained with a diverse vocabulary to accommodate different languages, dialects, and specialized terminology.
User-friendly interface: The intuitive interface ensures ease of use for both deaf individuals and those communicating with them.
In future iterations, SPARSHNET aims to enhance its capabilities by:
Improving accuracy: Refining Deep Learning models to better recognize a wider range of lip movements and speech patterns.
Multi-language support: Expanding language support to cater to diverse linguistic communities.
Mobile application: Developing a dedicated mobile application for improved accessibility and convenience.
Integration with other assistive technologies: Collaborating with other assistive technologies to provide a comprehensive solution for the deaf community.
MIT License
Copyright (c) 2024 Dhruv Kumar
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