Summary of Learning Sign Language Representation Using Cnn Lstm, 3dcnn, Cnn Rnn Lstm and Ccn Td, by Nikita Louison et al.
Learning Sign Language Representation using CNN LSTM, 3DCNN, CNN RNN LSTM and CCN TD
by Nikita Louison, Wayne Goodridge, Koffka Khan
First submitted to arxiv on: 24 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 explores algorithms for real-time video sign translation and grading of sign language accuracy for new users. It focuses on developing a system that can recognize and process spatial and temporal features, which is crucial for accurate sign language learning. The authors compare modern neural network algorithms like CNN and 3DCNN on a Trinidad and Tobago Sign Language (TTSL) dataset as well as an American Sign Language (ASL) dataset. They found that the 3DCNN algorithm outperforms others, achieving 91% accuracy in TTSL and 83% accuracy in ASL datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make sign language learning easier by creating a system that can translate and grade signs in real-time. This means students can practice signing without needing a teacher to check if they’re doing it correctly. The researchers tested different computer algorithms to see which one works best for this task. They looked at two types of algorithms, called CNN and 3DCNN, on special sign language datasets from Trinidad and Tobago and America. Surprisingly, the 3DCNN algorithm was the best performer, getting most signs correct in both datasets. |
Keywords
» Artificial intelligence » Cnn » Neural network » Translation
