FPGA-based translation system from colombian sign language to text

This paper presents the development of a system aimed to facilitate the communication and interaction of people with severe hearing impairment with other people. The system employs artificial vision techniques to the recognition of static signs of Colombian Sign Language (LSC). The system has four s...

Full description

Autores:
Guerrero Balaguera, Juan David
Pérez Holguín, Wilson Javier
Tipo de recurso:
Article of journal
Fecha de publicación:
2015
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/60779
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/60779
http://bdigital.unal.edu.co/59111/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
FPGA
Lengua de Señas Colombiana (LSC)
Procesamiento de Imágenes
Reconocimiento de Lengua de Señas
Redes Neuronales Artificiales
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:This paper presents the development of a system aimed to facilitate the communication and interaction of people with severe hearing impairment with other people. The system employs artificial vision techniques to the recognition of static signs of Colombian Sign Language (LSC). The system has four stages: Image capture, preprocessing, feature extraction and recognition. The image is captured by a digital camera TRDB-D5M for Altera’s DE1 and DE2 development boards. In the preprocessing stage, the sign is extracted from the background of the image using the thresholding segmentation method; then, the segmented image is filtered using a morphological operation to remove the noise. The feature extraction stage is based on the creation of two vectors to characterize the shape of the hand used to make the sign. The recognition stage is made up a multilayer perceptron neural network (MLP), which functions as a classifier. The system was implemented in the Altera’s Cyclone II FPGA EP2C70F896C6 device and does not require the use of gloves or visual markers for its proper operation. The results show that the system is able to recognize all the 23 signs of the LSC with a recognition rate of 98.15 %.