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Related Experiment Video

Updated: Jun 9, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Mexican sign language 3D static alphabet dataset.

Homero V Rios-Figueroa¹, Candy O Sosa-Jiménez²

¹Research Institute in Artificial Intelligence, Veracruzana University, Campus Sur, Calle Paseo Lote II, Sección Segunda 112, Nuevo Xalapa, Xalapa, Veracruz, C.P. 91097, Mexico.

|June 8, 2026

Summary

This study introduces a new dataset for Mexican Sign Language (MSL) recognition, featuring 3D coordinates of 21 static MSL alphabet letters. This resource aims to advance automatic MSL recognition research.

Keywords:

Computer vision Gesture recognition Hand gesture Machine learning Pattern recognition Sign language

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Last Updated: Jun 9, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Area of Science:

Computer Science
Linguistics
Human-Computer Interaction

Background:

Sign language facilitates communication for the hearing-impaired, with each country having its own variant.
Mexican Sign Language (MSL) research and available datasets are scarce, hindering automatic recognition advancements.

Purpose of the Study:

To address the lack of data for Mexican Sign Language (MSL) recognition.
To create and provide a novel dataset of static MSL alphabet signs.

Main Methods:

Collected a dataset of 21 static letters from the MSL alphabet.
Recorded 3D surface sensor data, capturing 3D coordinates for each sign.
Data acquired from 15 individuals performing each sign once using their right hand.

Main Results:

The resulting dataset comprises 315 text files.
Each file contains the 3D coordinates of static MSL alphabet letters.
The dataset captures detailed spatial information for each sign.

Conclusions:

The developed dataset is a valuable resource for MSL automatic recognition research.
This contribution aims to stimulate further investigation into MSL processing.
Availability of this data is expected to accelerate the development of MSL recognition technologies.