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Recognized by law, the Brazilian Sign Language (LIBRAS), is the second Brazilian official language and, according to IBGE (Brazilian Institute of Geography and Statistics), Brazil has a large community of hearing-impaired people, with approximately nine million of deaf people. Besides that, most of the non-deaf community cannot communicate or understand this language. Considering that, the use of LIBRAS’ interpreters becomes extremely necessary in order to allow a greater inclusion of people with this type of disability with the whole community. However, an alternative solution to this problem would be to use artificial neural network methods for the LIBRAS recognition and translation. In this work, a process of LIBRAS’ recognition and translation is presented, using videos as input and a convolutional-recurrent neural network, known as ConvLSTM. This type of neural network receives the sequence of frames from the videos and analyzes, frame by frame, if the frame belongs to the video and if the video belongs to a specific class. This analysis is done in two steps: first, the image is analyzed in the convolutional layer of the network and, after that, it is sent to the network recurrent layer. In the current version of the implemented network, data collection has already been carried out, the convolutional-recurrent neural network has been trained and it is possible to recognize when a given LIBRAS’ video represents or not a specific sentence in this language.

Processo para Reconhecimento e Tradução de Sinais em LIBRAS Utilizando Redes Neurais Artificiais