The automatic understanding of Sign Languages is a must to ease integration for millions of deaf people around the world. The last two decades have witnessed increasing research efforts to solve this problem. Many of these proposals were based on somehow intrusive sensors to capture the 3D rapid movements of arms, hands and fingers (data gloves, colored gloves, mocap, ultrasound, etc.), but, for the past ten years, RGB and depth sensors have been becoming the mainstream solution to simultaneously capturing the communicative channels related to hand movements, face expressivity and whole upper body movements. The latest advancements in human activity recognition from visual cues, rooted in highly efficient deep learning models, have also pushed research in Sign Language Recognition as a closely related application. We are in an excitement moment when it comes to pushing socially required applications to reduce communicative barriers.

This Special Issue seeks to bring together innovative research and development solutions in the area of Sign Language Recognition, using any kind of sensing devices. Comparative studies on different sensing devices are also very welcome. Authors are invited to submit original articles across the full development stack (hardware, system and software), including architectures, techniques and tools for sensing and modeling the complex movement details of signing and the proper decoding of sign sequences. This may include, but is not limited to, sensing modalities, innovative solutions for data collection, strategies for data augmentation, sensor fusion, spatio-temporal representation, computational reduction, model optimization for mobile devices, real-world applications, etc.

This topic is very well-fitted to the scope of the journal because using proper acquisition sensors allows trustful and discriminative information which is critical for sign language representation and, then, recognition. This journal also accepts the algorithmic processing of sensed signals, something that is rapidly evolving for SLR.

Guest Editors

Dr. José Luis Alba Castro
Dr. Sergio Escalera
Prof. Jun Wan

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