Malaysian Sign Language

This project focuses on developing a deep learning-based system for recognizing and translating Malaysian Sign Language (Bahasa Isyarat Malaysia, BIM) into text or speech. Leveraging convolutional neural networks (CNNs) and transformer models, the framework aims to accurately interpret dynamic hand gestures, facial expressions, and body movements unique to BIM. The system addresses real-time processing challenges and aims to enhance accessibility for the Deaf community in Malaysia, bridging communication gaps through technology. Key applications include educational tools, public service interfaces, and assistive devices, fostering inclusivity via automated sign language understanding.

本项目聚焦于开发基于深度学习的马来西亚手语（Malaysian Sign Language, BIM，全称Bahasa Isyarat Malaysia）识别与转写系统，可将其转化为文本或语音。本框架依托卷积神经网络（Convolutional Neural Networks, CNNs）与Transformer模型，旨在精准识别马来西亚手语特有的动态手部动作、面部表情与肢体姿态。该系统可应对实时处理的技术难题，致力于提升马来西亚听障群体的沟通可达性，通过技术手段弥合沟通鸿沟。其核心应用场景涵盖教育工具、公共服务交互界面与辅助设备，通过自动化手语理解技术助力社会包容发展。

搜集汇总

背景与挑战

背景概述

该数据集专注于开发基于深度学习的马来西亚手语（BIM）识别与翻译系统，利用CNN和Transformer模型解析手势、表情和身体动作，旨在提升聋人社区的沟通可及性，应用于教育和辅助技术领域。