Multi-scale feature extraction and nested-subset classifier designfor high accuracy handwritten character recognition
Jiayong Zhang
Xiaoqing Ding
Changsong Liu
Dept. of Electron. Eng., Tsinghua Univ., Beijing ;
This paper appears in: Pattern Recognition, 2000. Proceedings. 15th International Conference on
Publication Date: 2000
Volume: 2,
On page(s): 581-584 vol.2
Meeting Date: 09/03/2000 - 09/07/2000
Location: Barcelona, Spain
ISBN: 0-7695-0750-6
References Cited: 4
INSPEC Accession Number: 6887529
Digital Object Identifier: 10.1109/ICPR.2000.906141
Current Version Published: 2002-08-06
Abstract
Both efficient representation and robust classification are
essential to high-performance cursive offline handwritten Chinese
character recognition. A novel multi-scale feature extraction method is
presented based on the information entropy theory. Feature detection and
compression are thus combined into an integrated optimization process. A
series of optimal feature-spaces are constructed at varying values of
the scale parameter and the best one is obtained with the maximum LDA
criterion over the scale interval. For more robust classification, we
introduce a structure into the Mahalanobis distance classifier and
strike the balance between machine capacity and the performance on the
training data in light of the ideas of structural risk minimization. A
high accuracy recognition system is developed based on the new methods
and for the first time, 4 widely different databases ranging from
regular to completely unconstrained with several structural distortions
and stroke connections are fully tested. The accuracies of 99.S% on
regular database and 88.4% on cursive one at the speed of over 40
characters/s are achieved
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