Recognition of JSL finger spelling using convolutional neural networks

Abstract

Recently, a few methods for recognition of hand postures on depth maps using convolutional neural networks were proposed. In this paper, we present a framework for recognition of static finger spelling in Japanese Sign Language. The recognition takes place on the basis of single gray image. The finger spelled signs are recognized using a convolutional neural network. A dataset consisting of5000 samples has been recorded. A 3D articulated hand model has been designed to generate synthetic finger spellings and to extend the real hand gestures. Experimental results demonstrate that owing to sufficient amount of training data a high recognition rate can be attained on images from a single RGB camera. The full dataset and Caffe model are available for download.