Efficient Brazilian Sign Language Recognition: A Study on Mobile Devices

Automatic Sign Language Recognition (SLR) is a critical step in facilitating communication between deaf and hearing people. An interesting application of such a technology is a real-time mobile sign language translator since it could integrate both groups more easily. To this end,troduce a neBrazilian sign language (LIBRAS) recognition approach, the first for a mobile environment using an efficient 3D Convolutional Neural Network (CNN) to classify a sequence of frames extracted from a word being signaled in a video. Results show that our model is aproximately24 to 81 times faster than recent works in the field, and it is tested on a mobile device to understand the trade-off between performance and accuracy. Although slightly low accuracy, we have a significantly faster model at inference time and the beginning of something more relevant in the field, creating a discussion of future points of improvement to obtain an efficient real-time sign language system without greatly sacrificing accuracy in LIBRAS classification.