3DZSignDB：阿尔及利亚手语的三维头像SigML数据

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-04-25 DOI:10.1016/j.dib.2025.111568

Taha Zerrouki , Mohamed Fares Slimani , Amine Mami , Redha Mazari

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引用次数: 0

摘要

我们提出了一个阿尔及利亚手语（ALSL）的数据集，用于开发一个翻译系统到一个3D化身。我们使用主要基于汉堡符号系统（HamNoSys）的符号系统方法对417个ALSL词法符号进行编码。数据收集过程涉及与美国手语翻译专家的合作，以确保符号表示的一致性。NSM对每个手势进行转录，捕捉基本的语言特征，如手部形状、动作和面部表情。生成的数据集用于实时翻译系统，该系统将书面阿拉伯语转换为由3D化身表示的手语。该数据集的应用包括手语教育和聋人社区的辅助技术。你可以用它来改进自动手语翻译系统。所提供的结构化和注释的美国手语词典有助于不断发展的技术，使每个人都能交流，并在识别和合成手语方面取得更大进展。

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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3DZSignDB: 3D avatar SigML data for Algerian sign language

We present a dataset of Algerian Sign Language (ALSL) used to develop a translation system into a 3D avatar. We used the Notation System Method, which is mostly based on the Hamburg Notation System (HamNoSys), to encode 417 ALSL lexical signs. The data collection process involved collaboration with ALSL translation experts to ensure the consistency of sign representations.

NSM transcribed each sign, capturing essential linguistic features such as hand shape, movement, and facial expressions. The resulting dataset was used for a real-time translation system that converts written Arabic words into sign language represented by a 3D avatar.

Applications for the dataset include sign language education and assistive technologies for the deaf community. You can use it to improve systems for automatic sign language translation. The structured and annotated ALSL lexicon that is provided helps with the ongoing development of technologies that allow everyone to communicate and with making more progress in recognizing and synthesizing sign language.

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.