Summary of Real-time Bangla Sign Language Translator, by Rotan Hawlader Pranto et al.
Real-time Bangla Sign Language Translator
by Rotan Hawlader Pranto, Shahnewaz Siddique
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 BSLT (Bangla Sign Language Translation) paper proposes a system to bridge the communication gap between the deaf and mute community. It uses Mediapipe Holistic to extract key points from sign language, Long Short-Term Memory (LSTM) architecture for data training, and Computer Vision for real-time sign language detection with an impressive accuracy of 94%. The approach aims to enable seamless communication for this underserved group. The paper’s contributions include developing a novel system that combines computer vision and machine learning techniques to recognize Bangla sign language in real-time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The BSLT paper is all about helping people who are deaf or mute communicate better. It uses special tools like Mediapipe Holistic, LSTMs, and Computer Vision to understand sign language. This system can detect signs in real-time with 94% accuracy! Imagine being able to talk to someone without having to write everything down – that’s what this paper is trying to make possible. |
Keywords
» Artificial intelligence » Lstm » Machine learning » Translation
