Building a Compact On-Line MRF Recognizer for Large Character Set Using Structured Dictionary Representation and Vector Quantization Technique

This paper describes a method for building a compact on-line Markov random field (MRF) recognizer for large handwritten Japanese character set using structured dictionary representation and vector quantization (VQ) technique. The method splits character patterns into radicals, whose models by MRF are shared by different characters such that a character model is constructed from the constituent radical models. Many distinct radicals are shared by many characters with the result that the storage space of model dictionary can be saved. Moreover, in order to further compress the parameters, we employ VQ technique to cluster parameter sets of the mean vectors and covariance matrixes for MRF unary features and binary features as well as the transition probabilities of each state into groups. By sharing a common parameter set for each group, the dictionary of the MRF recognizer can be greatly compressed without recognition accuracy loss.