Automatic Synthesis of Training Data for Sign Language Recognition Using HMM

The paper describes a method of synthesizing sign language samples for training HMM. First face and hands regions are detected, and then features of sign language are extracted. For generating HMM, training data are automatically synthesized from a limited number of actual samples. We focus on the common hand shape in different word. The database hand shapes is generated and the training data of each word is synthesized by replacing the same shape in the database. Experiments using real image sequences are shown