Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Automatic Feature Generation for Handwritten Digit Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
A new system for reading handwritten zip codes
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Decision Tree Using Class-Dependent Feature Subsets
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Decision-Level Fusion in Fingerprint Verification
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Fuzzy model based recognition of handwritten numerals
Pattern Recognition
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A Robust Modular Wavelet Network Based Symbol Classifier
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
"Poor man" vote with M-ary classifiers: application to iris recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A GA-based feature selection algorithm for remote sensing images
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Taking advantage of class-specific feature selection
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
General framework for class-specific feature selection
Expert Systems with Applications: An International Journal
A data acquisition and analysis system for palm leaf documents in Telugu
Proceeding of the workshop on Document Analysis and Recognition
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In this paper, we propose a new approach to combine multiple features in handwriting recognition based on two ideas: feature selection-based combination and class-dependent features. A nonparametric method is used for feature evaluation, and the first part of this paper is devoted to the evaluation of features in terms of their class separation and recognition capabilities. In the second part, multiple feature vectors are combined to produce a new feature vector. Based on the fact that a feature has different discriminating powers for different classes, a new scheme of selecting and combining class-dependent features is proposed. In this scheme, a class is considered to have its own optimal feature vector for discriminating itself from the other classes. Using an architecture of modular neural networks as the classifier, a series of experiments were conducted on unconstrained handwritten numerals. The results indicate that the selected features are effective in separating pattern classes and the new feature vector derived from a combination of two types of such features further improves the recognition rate.