A Novel Mathematical Modeling And Parameterization For Sign Language Classification

Sign language recognition (SLR) has got wide applicability. SLR system is considered to be a challenging one. This paper presents empirical analysis of different mathematical models for Pakistan SLR (PSLR). The proposed method is using the parameterization of sign signature. Each sign is represented with a mathematical function and then coefficients of these functions are used as the feature vector. This approach is based on exhaustive experimentation and analysis for getting the best suitable mathematical representation for each sign. This extensive empirical analysis, results in a very small feature vector and hence to a very efficient system. The robust proposed method has got general applicability as it just need a new training set and it can work equally good for any other dataset. Sign set used is quite complex in the sense that intersign similarity distance is very small but even then proposed methodology has given quite promising results.