Real-time Bangla Sign Language Translator
- URL: http://arxiv.org/abs/2412.16497v1
- Date: Sat, 21 Dec 2024 05:56:32 GMT
- Title: Real-time Bangla Sign Language Translator
- Authors: Rotan Hawlader Pranto, Shahnewaz Siddique,
- Abstract summary: Bangla Sign Language Translation (BSLT) aims to bridge communication gaps for the deaf and mute community.
Our approach involves using Mediapipe Holistic to gather key points, LSTM architecture for data training, and Computer Vision for realtime sign language detection with an accuracy of 94%.
- Score: 0.3222802562733786
- License:
- Abstract: The human body communicates through various meaningful gestures, with sign language using hands being a prominent example. Bangla Sign Language Translation (BSLT) aims to bridge communication gaps for the deaf and mute community. Our approach involves using Mediapipe Holistic to gather key points, LSTM architecture for data training, and Computer Vision for realtime sign language detection with an accuracy of 94%. Keywords=Recurrent Neural Network, LSTM, Computer Vision, Bangla font.
Related papers
- Signs as Tokens: An Autoregressive Multilingual Sign Language Generator [55.94334001112357]
We introduce a multilingual sign language model, Signs as Tokens (SOKE), which can generate 3D sign avatars autoregressively from text inputs.
To align sign language with the LM, we develop a decoupled tokenizer that discretizes continuous signs into token sequences representing various body parts.
These sign tokens are integrated into the raw text vocabulary of the LM, allowing for supervised fine-tuning on sign language datasets.
arXiv Detail & Related papers (2024-11-26T18:28:09Z) - SCOPE: Sign Language Contextual Processing with Embedding from LLMs [49.5629738637893]
Sign languages, used by around 70 million Deaf individuals globally, are visual languages that convey visual and contextual information.
Current methods in vision-based sign language recognition ( SLR) and translation (SLT) struggle with dialogue scenes due to limited dataset diversity and the neglect of contextually relevant information.
We introduce SCOPE, a novel context-aware vision-based SLR and SLT framework.
arXiv Detail & Related papers (2024-09-02T08:56:12Z) - BAUST Lipi: A BdSL Dataset with Deep Learning Based Bangla Sign Language Recognition [0.5497663232622964]
Sign language research is burgeoning to enhance communication with the deaf community.
One significant barrier has been the lack of a comprehensive Bangla sign language dataset.
We introduce a new BdSL dataset comprising alphabets totaling 18,000 images, with each image being 224x224 pixels in size.
We devised a hybrid Convolutional Neural Network (CNN) model, integrating multiple convolutional layers, activation functions, dropout techniques, and LSTM layers.
arXiv Detail & Related papers (2024-08-20T03:35:42Z) - Scaling up Multimodal Pre-training for Sign Language Understanding [96.17753464544604]
Sign language serves as the primary meaning of communication for the deaf-mute community.
To facilitate communication between the deaf-mute and hearing people, a series of sign language understanding (SLU) tasks have been studied.
These tasks investigate sign language topics from diverse perspectives and raise challenges in learning effective representation of sign language videos.
arXiv Detail & Related papers (2024-08-16T06:04:25Z) - EvSign: Sign Language Recognition and Translation with Streaming Events [59.51655336911345]
Event camera could naturally perceive dynamic hand movements, providing rich manual clues for sign language tasks.
We propose efficient transformer-based framework for event-based SLR and SLT tasks.
Our method performs favorably against existing state-of-the-art approaches with only 0.34% computational cost.
arXiv Detail & Related papers (2024-07-17T14:16:35Z) - SignSpeak: Open-Source Time Series Classification for ASL Translation [0.12499537119440243]
We propose a low-cost, real-time ASL-to-speech translation glove and an exhaustive training dataset of sign language patterns.
We benchmarked this dataset with supervised learning models, such as LSTMs, GRUs and Transformers, where our best model achieved 92% accuracy.
Our open-source dataset, models and glove designs provide an accurate and efficient ASL translator while maintaining cost-effectiveness.
arXiv Detail & Related papers (2024-06-27T17:58:54Z) - Connecting the Dots: Leveraging Spatio-Temporal Graph Neural Networks
for Accurate Bangla Sign Language Recognition [2.624902795082451]
We present a new word-level Bangla Sign Language dataset - BdSL40 - consisting of 611 videos over 40 words.
This is the first study on word-level BdSL recognition, and the dataset was transcribed from Indian Sign Language (ISL) using the Bangla Sign Language Dictionary (1997).
The study highlights the significant lexical and semantic similarity between BdSL, West Bengal Sign Language, and ISL, and the lack of word-level datasets for BdSL in the literature.
arXiv Detail & Related papers (2024-01-22T18:52:51Z) - All You Need In Sign Language Production [50.3955314892191]
Sign language recognition and production need to cope with some critical challenges.
We present an introduction to the Deaf culture, Deaf centers, psychological perspective of sign language.
Also, the backbone architectures and methods in SLP are briefly introduced and the proposed taxonomy on SLP is presented.
arXiv Detail & Related papers (2022-01-05T13:45:09Z) - Skeleton Based Sign Language Recognition Using Whole-body Keypoints [71.97020373520922]
Sign language is used by deaf or speech impaired people to communicate.
Skeleton-based recognition is becoming popular that it can be further ensembled with RGB-D based method to achieve state-of-the-art performance.
Inspired by the recent development of whole-body pose estimation citejin 2020whole, we propose recognizing sign language based on the whole-body key points and features.
arXiv Detail & Related papers (2021-03-16T03:38:17Z) - Novel Approach to Use HU Moments with Image Processing Techniques for
Real Time Sign Language Communication [0.0]
"Sign Language Communicator" (SLC) is designed to solve the language barrier between the sign language users and the rest of the world.
System is able to recognize selected Sign Language signs with the accuracy of 84% without a controlled background with small light adjustments.
arXiv Detail & Related papers (2020-07-20T03:10:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.