Hand movement recognition for brazilian sign language: a study using distance-based neural networks

In this paper, the vision-based hand movement recognition problem is formulated for the universe of discourse of the Brazilian Sign Language. In order to analyze this specific domain we have used the artificial neural networks models based on distance, including neural-fuzzy models. The experiments explored here show the usefulness of these models to extract helpful knowledge about the classes of movements and to support the project of adaptative recognizer modules for Libras-oriented computational tools. Using artificial neural networks architectures - Self Organizing Maps and (Fuzzy) Learning Vector Quantization, it was possible to understand the data space and to build models able to recognize hand movements performed for one or more than one specific Libras users.