Multilevel HMM for handwritten word recognition

We introduce a novel approach for handwritten word recognition using multilevel hidden Markov models (MLHMM). The MLHMM is a doubly embedded network of HMMs where each character is modeled by an HMM while a word is modeled by a higher-level HMM. In the character model, we associate the observation with the transition. By introducing the technique called `tied transition´, i.e., the segments which have the same semantic meaning will be `tied´ together, we have successfully built up the character model by an HMM with 4 states, 5 observations (or symbols) and 7 transitions. Thus, as the states are not assigned any semantic meaning, the re-estimation algorithm is applicable. At the character level, the best model is chosen as the recognition result. So, the character model is purely a model discriminant HMM (MD-HMM) based approach. For the word model, on the other hand, both the MD-HMM and the path discriminant HMM (PD-HMM) approaches are used and their respective performances are demonstrated