Title :
Building a Compact On-Line MRF Recognizer for Large Character Set Using Structured Dictionary Representation and Vector Quantization Technique
Author :
Bilan Zhu ; Nakagawa, Masaki
Author_Institution :
Dept. of Comput. & Inf. Sci., Tokyo Univ. of Agric. & Technol., Koganei, Japan
Abstract :
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.
Keywords :
Markov processes; character sets; covariance matrices; dictionaries; handwritten character recognition; natural language processing; probability; storage management; vector quantisation; vectors; MRF unary features; VQ technique; binary features; character model; character patterns; cluster parameter sets; common parameter set; compact online MRF recognizer; covariance matrixes; distinct radicals; handwritten Japanese character set; mean vectors; model dictionary; online Markov random field recognizer; radical models; recognition accuracy loss; storage space; structured dictionary representation; transition probability; vector quantization technique; Character recognition; Covariance matrix; Dictionaries; Feature extraction; Handwriting recognition; Hidden Markov models; Vectors;
Conference_Titel :
Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference on
Conference_Location :
Bari
Print_ISBN :
978-1-4673-2262-1
DOI :
10.1109/ICFHR.2012.188