Sign Language Detection Using Machine Learning
Unlocking Communication: Empowering Through Hand Sign Detection
Created on 24th February 2024
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Sign Language Detection Using Machine Learning
Unlocking Communication: Empowering Through Hand Sign Detection
The problem Sign Language Detection Using Machine Learning solves
The Hand Sign Detection project addresses the critical issue of communication accessibility for individuals who use sign language as their primary mode of expression. By offering real-time translation of sign language into spoken or written language, it eliminates the language barrier and enables seamless communication between sign language users and those who don't understand sign language. This groundbreaking technology promotes inclusivity in educational, professional, and social settings, empowering individuals with hearing impairments to express themselves confidently and engage meaningfully with others. Additionally, by integrating hand sign detection into mainstream technology platforms, it ensures that everyone can participate fully in the digital world, regardless of their communication preferences or abilities. Ultimately, this project revolutionizes communication accessibility, fostering independence, empowerment, and inclusivity for individuals of all abilities.
Challenges I ran into
The project encountered performance issues in suboptimal lighting conditions, a vital concern for real-world applicability. I iteratively refined the model by incorporating diverse lighting scenarios into the training dataset, ensuring its adaptability to varying environmental conditions. Striving for a lightweight solution, I encountered accuracy challenges in complex environments with multiple elements in the background. This predicament spurred my exploration of alternative classifiers, such as the Naive Bayes algorithm, providing insights into optimizing model efficiency without compromising accuracy.
Technologies used