Reconnaissance Automatique des Langues des Signes : Une Approche Hybridée CNN-LSTM Basée sur Mediapipe

• URL: http://arxiv.org/abs/2510.22011v1
• Date: Fri, 24 Oct 2025 20:25:25 GMT
• Title: Reconnaissance Automatique des Langues des Signes : Une Approche Hybridée CNN-LSTM Basée sur Mediapipe
• Abstract summary: Sign languages play a crucial role in the communication of deaf communities, but they are often marginalized.<n>This study proposes an automatic sign language system based on a hybrid CNN-LSTM architecture, using Mediapipe for keypoint extraction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sign languages play a crucial role in the communication of deaf communities, but they are often marginalized, limiting access to essential services such as healthcare and education. This study proposes an automatic sign language recognition system based on a hybrid CNN-LSTM architecture, using Mediapipe for gesture keypoint extraction. Developed with Python, TensorFlow and Streamlit, the system provides real-time gesture translation. The results show an average accuracy of 92\%, with very good performance for distinct gestures such as ``Hello'' and ``Thank you''. However, some confusions remain for visually similar gestures, such as ``Call'' and ``Yes''. This work opens up interesting perspectives for applications in various fields such as healthcare, education and public services.

Related papers

• Indian Sign Language Detection for Real-Time Translation using Machine Learning [0.1747623282473278]
  We propose a robust, real-time ISL detection & translation system built upon a Convolutional Neural Network (CNN)<n>Our model is trained on a comprehensive ISL dataset & demonstrates exceptional performance, achieving a classification accuracy of 99.95%.<n>For real-time implementation, the framework integrates MediaPipe for precise hand tracking & motion detection, enabling seamless translation of dynamic gestures.
  arXiv  Detail & Related papers  (2025-07-27T21:15:46Z)
• SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction [65.1590372072555]
  SHuBERT (Sign Hidden-Unit BERT) is a self-supervised contextual representation model learned from 1,000 hours of American Sign Language video.<n>SHuBERT adapts masked token prediction objectives to multi-stream visual sign language input, learning to predict multiple targets corresponding to clustered hand, face, and body pose streams.<n>SHuBERT achieves state-of-the-art performance across multiple tasks including sign language translation, isolated sign language recognition, and fingerspelling detection.
  arXiv  Detail & Related papers  (2024-11-25T03:13:08Z)
• 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)
• Enhancing Sign Language Detection through Mediapipe and Convolutional Neural Networks (CNN) [3.192629447369627]
  This research combines MediaPipe and CNNs for the efficient and accurate interpretation of ASL dataset. The accuracy achieved by the model on ASL datasets is 99.12%. The system will have applications in the communication, education, and accessibility domains.
  arXiv  Detail & Related papers  (2024-06-06T04:05:12Z)
• Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation [3.976851945232775]
  Current approaches for sign language recognition rely on RGB video inputs, which are vulnerable to fluctuations in the background. We propose a multi-stream keypoint attention network to depict a sequence of keypoints produced by a readily available keypoint estimator. We carry out comprehensive experiments on well-known benchmarks like Phoenix-2014, Phoenix-2014T, and CSL-Daily to showcase the efficacy of our methodology.
  arXiv  Detail & Related papers  (2024-05-09T10:58:37Z)
• Mediapipe and CNNs for Real-Time ASL Gesture Recognition [0.1529342790344802]
  This research paper describes a realtime system for identifying American Sign Language (ASL) movements. The suggested method makes use of the Mediapipe library for feature extraction and a Convolutional Neural Network (CNN) for ASL gesture classification.
  arXiv  Detail & Related papers  (2023-05-09T09:35:45Z)
• WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language [2.814213966364155]
  We build a large-scale dataset of American Sign Language signs annotated with six different phonological properties. We investigate whether data-driven end-to-end and feature-based approaches can be optimised to automatically recognise these properties.
  arXiv  Detail & Related papers  (2022-03-11T17:21:24Z)
• Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble [71.97020373520922]
  Sign language is commonly used by deaf or mute people to communicate. We propose a novel Multi-modal Framework with a Global Ensemble Model (GEM) for isolated Sign Language Recognition ( SLR) Our proposed SAM- SLR-v2 framework is exceedingly effective and achieves state-of-the-art performance with significant margins.
  arXiv  Detail & Related papers  (2021-10-12T16:57:18Z)
• 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)
• Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages [51.0542215642794]
  We propose a novel acoustics based intent recognition system that uses discovered phonetic units for intent classification. We present results for two languages families - Indic languages and Romance languages, for two different intent recognition tasks.
  arXiv  Detail & Related papers  (2020-11-07T00:35:31Z)
• Transferring Cross-domain Knowledge for Video Sign Language Recognition [103.9216648495958]
  Word-level sign language recognition (WSLR) is a fundamental task in sign language interpretation. We propose a novel method that learns domain-invariant visual concepts and fertilizes WSLR models by transferring knowledge of subtitled news sign to them.
  arXiv  Detail & Related papers  (2020-03-08T03:05:21Z)

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.