Gesture recognition for fingerspelling applications: an approach based on sign language cheremes

Authors:
Renata C. B. Madeo;Sarajane M. Peres;Daniel B. Dias;Clodis Boscarioli
Affiliations:
University of São Paulo, São Paulo, Brazil;University of São Paulo, São Paulo, Brazil;University of São Paulo, São Paulo, Brazil;West. Paraná State University, Cascavel, Brazil
Venue:
Proceedings of the 12th international ACM SIGACCESS conference on Computers and accessibility
Year:
2010

Citing 5
Cited 1

A Survey of Glove-based Input

IEEE Computer Graphics and Applications
Toward Scalability in ASL Recognition: Breaking Down Signs into Phonemes

GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
Hand movement recognition for brazilian sign language: a study using distance-based neural networks

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A committee machine implementing the pattern recognition module for fingerspelling applications

Proceedings of the 2010 ACM Symposium on Applied Computing
Gesture Recognition: A Survey

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Brazilian sign language multimedia hangman game: a prototype of an educational and inclusive application

The proceedings of the 13th international ACM SIGACCESS conference on Computers and accessibility

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an approach for carrying out gesture recognition for the Brazilian Sign Language Manual Alphabet. The gestural patterns are treated as a combination of three primitives, or cheremes - hand configuration, hand orientation and hand movement. The recognizer is built in a modular architecture composed by inductive reasoning modules, which use the artificial neural network Fuzzy Learning Vector Quantization; and rule-based modules. This architecture has been tested and results are presented here. Some strengths of such approach are: robustness of recognition, portability to similar contexts, extensibility of the dataset to be recognize and reduction of the vocabulary recognition problem to the recognition of its primitives.