SCHMM for Sign Language Recognition
A Semi-Continuous Hidden Markov Model for sign language recognition is presented, shows that SCHMM is prior to the DHMM and the CHMM in theory and debase the complexity and the operations at the same recognition rate.
Abstract
The research of sign language recognition has great academic value and broad application prospect.In recent works on sign language recognition,Hidden Markov Models(HMMs) has played an important role.The statistical frame based on the HMM is the mainstream method in dynamic recognition domain recently;also is this article's basic theory.Presents a Semi-Continuous Hidden Markov Model for sign language recognition,shows that SCHMM is prior to the DHMM and the CHMM in theory.SCHMM avoid the information loss because of the vector estimate in DHMM;debase the complexity and the operations at the same recognition rate.