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SIIA Público
SISTEMA INTEGRAL DE INFORMACIÓN ACADÉMICA - PÚBLICO
Título del libro: 2021 Mexican International Conference On Computer Science, Enc 2021 Título del capítulo: A real-time deep learning system for the translation of mexican signal language into text
Autores UNAM: JOSE ANTONIO NEME CASTILLO;
Autores externos: Idioma: Año de publicación: 2021Palabras clave:
Sign language interpretation; Machine learning; Convolutional neural networks; Deep Learning; Transfer learning; Singular Page Application; Flask
Resumen:
The ability to communicate is one of the most important rights of a
healthy individual. Hearing-impaired citizens face several challenges:
There is not a common sign language and the percentage of the population
that understands a given sign language is negligible. The benefits of a
system capable of translating from a sign language, in particular, the
Mexican Sign language, to text, is outstanding. In this contribution, we
describe such system. It is based on convolutional neural networks,
which are particularly useful tool to extract relevant features from
images. The sign language is captured in hundreds of images, and the
system is able to translate them to text via several convolutional
stages and transfer learning with 91% accuracy. We report the
architecture and performance of the system.
Entidades citadas de la UNAM:
Fuente:
ISBN: 9781665426121 Editorial: Institute of Electrical and Electronics Engineers Inc.
