Handshape recognition for Argentinian Sign Language using ProbSom

Título:

Handshape recognition for Argentinian Sign Language using ProbSom

Autor:

Ronchetti, Franco

Colaboradores:

; Quiroga, Facundo Manuel; Estrebou, César Armando; Lanzarini, Laura Cristina

Temas:

RESPONSABILIDAD SOCIALPROCESAMIENTO DE IMÁGENES

En:

Journal of Computer Science & Technology, 16(1), pp. 1-5.

Resumen:

Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.

Palabras clave:

ProbSomLenguaje de Señas Argentino

Medio:

Soporte electrónico

Tipo de documento:

Artículo

Descripción física:

1 archivo (474,1 kB)

Idioma:

Inglés

Publicación:

, 2016

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