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

Fraisse Sacré Takouchouang, Ho Tuong Vinh
10/24/2025
cs.CVcs.AI

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Sign Language Recognition based on YOLOv5 Algorithm for the Telugu Sign Language

89% match2024

Continuous Sign Language Recognition System using Deep Learning with MediaPipe Holistic

87% match2024

Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation

86% match1 repo402024

Indian Sign Language Detection for Real-Time Translation using Machine Learning

86% match2025

Sign language recognition based on deep learning and low-cost handcrafted descriptors

85% match2024

Sign Language Translation using Frame and Event Stream: Benchmark Dataset and Algorithms

85% match1 repo202025

Cite this paper

@article{takouchouang2025reconnaissance,
  title  = {Reconnaissance Automatique des Langues des Signes : Une Approche Hybridée CNN-LSTM Basée sur Mediapipe},
  author = {Fraisse Sacré Takouchouang and Ho Tuong Vinh},
  year   = {2025},
  eprint = {2510.22011},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.22011v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2510.22011.svg)](https://codeofpaper.com/paper/2510.22011)

Discussion