Razieh Rastgoo author Kourosh Kiani author Sergio Escalera author 2018 In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets. hand sign language deep learning restricted Boltzmann machine (RBM) multi-modal profoundly deaf noisy image HUPBA; no proj;MILAB exported from refbase (http://refbase.cvc.uab.es/show.php?record=3198), last updated on Tue, 02 Jul 2019 13:47:48 +0200 text 10.3390/e20110809 Admin @ si @ RKE2018 2018 continuing periodical academic journal 20 11 809