Martínez-Sánchez, V.; Villalón-Turrubiates, I.; Cervantes-Álvarez, F.; Hernández-Mejía, C. Exploring a Novel Mexican Sign Language Lexicon Video Dataset. Multimodal Technol. Interact.2023, 7, 83.
Martínez-Sánchez, V.; Villalón-Turrubiates, I.; Cervantes-Álvarez, F.; Hernández-Mejía, C. Exploring a Novel Mexican Sign Language Lexicon Video Dataset. Multimodal Technol. Interact. 2023, 7, 83.
Martínez-Sánchez, V.; Villalón-Turrubiates, I.; Cervantes-Álvarez, F.; Hernández-Mejía, C. Exploring a Novel Mexican Sign Language Lexicon Video Dataset. Multimodal Technol. Interact.2023, 7, 83.
Martínez-Sánchez, V.; Villalón-Turrubiates, I.; Cervantes-Álvarez, F.; Hernández-Mejía, C. Exploring a Novel Mexican Sign Language Lexicon Video Dataset. Multimodal Technol. Interact. 2023, 7, 83.
Abstract
In Mexico, the incorporation of deaf people into education has been lacking since only 14% of the deaf population in the age group between 3 and 29 years access education with the support of a hearing aid. Additionally, those who have been incorporated frequently face inappropriate educational strategies which poorly develop the use of Mexican Sign Language (MSL) and therefore academical success and opportunities for insertion in the workplace are difficult. This research explores a novel mexican sign language lexicon video dataset containing the dynamical gestures most frequently used by MSL. Each gesture consists of a set of different versions of videos under uncontrolled conditions. MX-ITESO-100 data set is composed of a lexicon of 100 gestures and 5,000 videos from three participants with different grammatical elements. Additionally, the data set is evaluated in a two-step neural network model with an accuracy greater than 99%. and thus serves as a benchmark for future training of machine learning models in computer vision systems. Finally, this research provides an inclusive environment within society and organizations in particular for people with hearing impairment.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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