Summary of Real-time Sign Language Recognition Using Mobilenetv2 and Transfer Learning, by Smruti Jagtap et al.
Real-time Sign Language Recognition Using MobileNetV2 and Transfer Learning
by Smruti Jagtap, Kanika Jadhav, Rushikesh Temkar, Minal Deshmukh
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to develop a reliable Indian Sign Language (ISL) recognition system using Convolutional Neural Networks (CNNs). The goal is to bridge the gap in communication access for individuals with hearing disabilities, enabling them to participate more fully in social and educational arenas. Building on the limited existing technology solutions, the researchers aim to create an efficient system that converts ISL signals into speech or text, thereby promoting greater inclusivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to develop a reliable Indian Sign Language (ISL) recognition system using Convolutional Neural Networks (CNNs). This is important because many people with hearing disabilities in India lack access to communication tools. The researchers want to fill this gap and make it easier for people who use ISL to communicate. |
