Towards a Bidirectional Mexican Sign Language–Spanish Translation System: A Deep Learning Approach

IF 4.2 Q1 ENGINEERING, MULTIDISCIPLINARY Technologies Pub Date : 2024-01-05 DOI:10.3390/technologies12010007
Jaime-Rodrigo González-Rodríguez, Diana-Margarita Córdova-Esparza, Juan R. Terven, J. Romero-González
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Abstract

People with hearing disabilities often face communication barriers when interacting with hearing individuals. To address this issue, this paper proposes a bidirectional Sign Language Translation System that aims to bridge the communication gap. Deep learning models such as recurrent neural networks (RNN), bidirectional RNN (BRNN), LSTM, GRU, and Transformers are compared to find the most accurate model for sign language recognition and translation. Keypoint detection using MediaPipe is employed to track and understand sign language gestures. The system features a user-friendly graphical interface with modes for translating between Mexican Sign Language (MSL) and Spanish in both directions. Users can input signs or text and obtain corresponding translations. Performance evaluation demonstrates high accuracy, with the BRNN model achieving 98.8% accuracy. The research emphasizes the importance of hand features in sign language recognition. Future developments could focus on enhancing accessibility and expanding the system to support other sign languages. This Sign Language Translation System offers a promising solution to improve communication accessibility and foster inclusivity for individuals with hearing disabilities.
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墨西哥手语-西班牙语双向翻译系统:深度学习方法
听力残疾人士在与健听人士交流时经常会遇到沟通障碍。为解决这一问题,本文提出了一种双向手语翻译系统,旨在消除沟通障碍。本文比较了递归神经网络(RNN)、双向 RNN(BRNN)、LSTM、GRU 和 Transformers 等深度学习模型,以找到最准确的手语识别和翻译模型。使用 MediaPipe 进行关键点检测,以跟踪和理解手语手势。该系统具有用户友好的图形界面,可在墨西哥手语 (MSL) 和西班牙语之间进行双向翻译。用户可以输入手势或文本,并获得相应的翻译。性能评估显示了较高的准确率,BRNN 模型的准确率达到 98.8%。这项研究强调了手部特征在手语识别中的重要性。未来的发展重点是提高系统的可访问性,并将其扩展到支持其他手语。该手语翻译系统为改善听力残疾人士的无障碍交流和促进包容性提供了一个前景广阔的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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