American Sign Language Fingerspelling Recognition Using Wide Residual Networks

Despite existing solutions for accurate translation between written and spoken language, sign language is still not well-studied area. A reliable, robust and working in real-time translator of American Sign Language is a crucial bridge to facilitate communication between deaf and hearing people. In this paper we propose a method of sign language fingerspelling recognition using a modern architecture of convolutional neural network called Wide Residual Network trained with Snapshot Learning procedure. The model was trained on augmented datasets available at Surrey University and Massey University web pages using transfer learning. The final result is a robust classifier of all alphabet letters, which beats current state-of-the-art results. The outcomes encourage further research in this field for creating fully usable sign language translator.

In ICAISC 2018 Artificial Intelligence and Soft Computing pp 97-107