Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten Chinese Character Recognition: Alternatives to Nonlinear Normalization
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Handwritten Kanji Recognition with the LDA Method
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
An improved handwritten Chinese character recognition system using support vector machine
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
LDA-Based Compound Distance for Handwritten Chinese Character Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Pattern recognition using discriminative feature extraction
IEEE Transactions on Signal Processing
Multilinguistic handwritten character recognition by Bayesiandecision-based neural networks
IEEE Transactions on Signal Processing
Discriminative learning quadratic discriminant function for handwriting recognition
IEEE Transactions on Neural Networks
Recognition of handwritten Chinese characters by critical region analysis
Pattern Recognition
International Journal of Applied Mathematics and Computer Science
A recognition system for online handwritten tibetan characters
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Similar handwritten Chinese character recognition by kernel discriminative locality alignment
Pattern Recognition Letters
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To improve the accuracy of handwritten Chinese character recognition (HCCR), we propose linear discriminant analysis (LDA)-based compound distances for discriminating similar characters. The LDA-based method is an extension of previous compound Mahalanobis function (CMF), which calculates a complementary distance on a one-dimensional subspace (discriminant vector) for discriminating two classes and combines this complementary distance with a baseline quadratic classifier. We use LDA to estimate the discriminant vector for better discriminability and show that under restrictive assumptions, the CMF is a special case of our LDA-based method. Further improvements can be obtained when the discriminant vector is estimated from higher-dimensional feature spaces. We evaluated the methods in experiments on the ETL9B and CASIA databases using the modified quadratic discriminant function (MQDF) as baseline classifier. The results demonstrate the superiority of LDA-based method over the CMF and the superiority of discriminant vector learning from high-dimensional feature spaces. Compared to the MQDF, the proposed method reduces the error rates by factors of over 26%.