Summary of Continuous Sign Language Recognition System Using Deep Learning with Mediapipe Holistic, by Sharvani Srivastava et al.
Continuous Sign Language Recognition System using Deep Learning with MediaPipe Holistic
by Sharvani Srivastava, Sudhakar Singh, Pooja, Shiv Prakash
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed continuous sign language recognition (SLR) system uses a deep learning model based on Long Short-Term Memory (LSTM) to translate Indian Sign Language (ISL) into vocal language. The system is trained and tested on an ISL primary dataset created using MediaPipe Holistic pipeline, which tracks face, hand, and body movements and collects landmarks. The SLR system recognizes signs and gestures in real-time with 88.23% accuracy, addressing the issue of limited familiarity with sign languages that hinders communication between hearing-impaired people and others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to recognize sign language. They used a special kind of artificial intelligence called deep learning to create a system that can translate Indian Sign Language into spoken language in real-time. This is important because many people who are deaf or hard of hearing struggle to communicate with those who can hear. The new system is very accurate, getting 88% of signs and gestures right. |
Keywords
» Artificial intelligence » Deep learning » Lstm
