The Strength of Weak Learnability
Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Cursive character recognition by learning vector quantization
Pattern Recognition Letters
Optical Character Recognition for Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten Word Recognition based on Structural Characteristics and Lexical Support
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Boosted decision trees for word recognition in handwritten document retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Rapid and brief communication: FuzzyBagging: A novel ensemble of classifiers
Pattern Recognition
Reliable recognition of handwritten digits using a cascade ensemble classifier system and hybrid features
A SVM-based cursive character recognizer
Pattern Recognition
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binary segmentation with neural validation for cursive handwriting recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Non-uniform layered clustering for ensemble classifier generation and optimality
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
An overview of character recognition focused on off-line handwriting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper we present novel ensemble classifier architectures and investigate their influence for offline cursive character recognition. Cursive characters are represented by feature sets that portray different aspects of character images for recognition purposes. The recognition accuracy can be improved by training ensemble of classifiers on the feature sets. Given the feature sets and the base classifiers, we have developed multiple ensemble classifier compositions under four architectures. The first three architectures are based on the use of multiple feature sets whereas the fourth architecture is based on the use of a unique feature set. Type-1 architecture is composed of homogeneous base classifiers and Type-2 architecture is constructed using heterogeneous base classifiers. Type-3 architecture is based on hierarchical fusion of decisions. In Type-4 architecture a unique feature set is learned by a set of homogeneous base classifiers with different learning parameters. The experimental results demonstrate that the recognition accuracy achieved using the proposed ensemble classifier (with best composition of base classifiers and feature sets) is better than the existing recognition accuracies for offline cursive character recognition.