This paper discusses sign language recognition using linguistic sub-units. It presents three
types of sub-units for consideration; those learnt from appearance data as well as those …

This paper presents a novel approach to sign language recognition that provides extremely
high classification rates on minimal training data. Key to this approach is a 2 stage …

This paper presents a novel, discriminative, multi-class classifier based on Sequential
Pattern Trees. It is efficient to learn, compared to other Sequential Pattern methods, and …

The major challenge that faces American Sign Language (ASL) recognition now is
developing methods that will scale well with increasing vocabulary size. Unlike in spoken …

Sign language recognition constitutes a challenging field of research in computer vision.
Common problems like overlap, ambiguities, and minimal pairs occur frequently and require …

This paper presents an automatic Australian sign language (Auslan) recognition system,
which tracks multiple target objects (the face and hands) throughout an image sequence …

We introduce a new computational phonetic modeling framework for sign language (SL)
recognition. This is based on dynamic–static statistical subunits and provides sequentiality …

Research in the field of sign language recognition has made significant advances in recent
years. The present achievements provide the basis for future applications with the objective …

Recently, great progress has been made in sign language recognition. Most approaches are
based on the Hidden Markov Model (HMM) with various features, such as motion trajectory …

A new, linguistically annotated, video database for automatic sign language recognition is
presented. The new RWTH-BOSTON-400 corpus, which consists of 843 sentences, several …