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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera edit  url
doi  openurl
  Title Video-based Isolated Hand Sign Language Recognition Using a Deep Cascaded Model Type Journal Article
  Year 2020 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 79 Issue Pages 22965–22987  
  Abstract In this paper, we propose an efficient cascaded model for sign language recognition taking benefit from spatio-temporal hand-based information using deep learning approaches, especially Single Shot Detector (SSD), Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM), from videos. Our simple yet efficient and accurate model includes two main parts: hand detection and sign recognition. Three types of spatial features, including hand features, Extra Spatial Hand Relation (ESHR) features, and Hand Pose (HP) features, have been fused in the model to feed to LSTM for temporal features extraction. We train SSD model for hand detection using some videos collected from five online sign dictionaries. Our model is evaluated on our proposed dataset (Rastgoo et al., Expert Syst Appl 150: 113336, 2020), including 10’000 sign videos for 100 Persian sign using 10 contributors in 10 different backgrounds, and isoGD dataset. Using the 5-fold cross-validation method, our model outperforms state-of-the-art alternatives in sign language recognition  
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  Notes HuPBA; no menciona Approved no  
  Call Number Admin @ si @ RKE2020b Serial 3442  
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