B. Ashwanth
Papers
1
Total Citations
3
H-Index
1
About
B. Ashwanth is a rising researcher in the field of computer vision and human-computer interaction, with a focused interest in assistive technology for the hearing impaired. His most-cited work, "Vision-based Hand Gesture Recognition for Indian Sign Language Using Convolution Neural Network" (2023), has already garnered 3 citations, demonstrating early impact in this specialized domain. This paper addresses a critical accessibility challenge by developing a CNN-based system capable of interpreting Indian Sign Language (ISL) gestures in real-time, bridging communication gaps between the deaf and hearing communities. Ashwanth's approach leverages deep learning to achieve robust gesture classification without the need for expensive sensor hardware, making the technology more accessible for widespread adoption. His work contributes to the growing body of research on low-cost, vision-based sign language recognition systems, particularly for under-resourced languages like ISL. As a young researcher, Ashwanth's contributions signal a promising trajectory in applying artificial intelligence to solve real-world social challenges, with potential applications extending to other gesture-based interfaces and inclusive technology design.
Research Focus
Key Achievements
Top Papers
- 1