Resumen
The proposed research deals with constructing a sign gesture recognition system to enable improved interaction between sign and non-sign users. With respect to this goal, five types of features are utilized?hand coordinates, convolutional features, convolutional features with finger angles, convolutional features on hand edges and convolutional features on binary robust invariant scalable keypoints?and trained on ensemble classifiers to accurately predict the label of the sign image provided as input. In addition, a hybrid artificial neural network is also fabricated that takes two of the aforementioned features, namely convolutional features and convolutional features on hand edges to precisely locate the hand region of the sign gesture under consideration in an attempt for classification. Experiments are also performed with convolutional neural networks on those benchmark datasets which are not accurately classified by the previous two methods. Overall, the proposed methodologies are able to handle a diverse variety of images that include labyrinthine backgrounds, user-specific distinctions, minuscule discrepancies between classes and image alterations. As a result, they are able to produce accuracies comparable with state-of-the-art literature.