Summary of Enhancing Sign Language Detection Through Mediapipe and Convolutional Neural Networks (cnn), by Aditya Raj Verma et al.
Enhancing Sign Language Detection through Mediapipe and Convolutional Neural Networks (CNN)
by Aditya Raj Verma, Gagandeep Singh, Karnim Meghwal, Banawath Ramji, Praveen Kumar Dadheech
First submitted to arxiv on: 6 Jun 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 proposed system combines MediaPipe and Convolutional Neural Networks (CNNs) for accurate real-time detection of sign language in American Sign Language (ASL) datasets. The goal is to create an efficient and easy way to enter commands without touching screens, utilizing the powerful frameworks of real-time hand tracking capabilities. This integration results in higher efficiency, achieving 99.12% accuracy on ASL datasets. The system is tested using established evaluation techniques and has applications in communication, education, and accessibility domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new system uses computer vision to recognize sign language in real-time. It combines two powerful tools: MediaPipe and Convolutional Neural Networks (CNNs). This helps people with hearing impairments communicate easily. The system is tested on a big dataset of American Sign Language (ASL) signs, showing it can understand 99% of them correctly. This technology has many uses in areas like education and communication. |
Keywords
» Artificial intelligence » Tracking
