The Strength of Weak Learnability
Machine Learning
Holistic handwritten word recognition using temporal features derived from off-line images
Pattern Recognition Letters
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Baseline Estimation For Arabic Handwritten Words
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Automatic fuzzy rule base generation for on-line handwritten alphanumeric character recognition
International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers from the KES2004 conference
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
Pattern Recognition Letters
Guest Editorial: Information fusion in computer security
Information Fusion
Editorial: New Frontiers in Handwriting Recognition
Pattern Recognition
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
ICDAR 2009 Arabic Handwriting Recognition Competition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Classifiers combination and syntax analysis for Arabic literal amount recognition
Engineering Applications of Artificial Intelligence
A Measure of Competence Based on Randomized Reference Classifier for Dynamic Ensemble Selection
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Dynamic ensemble selection for off-line signature verification
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Dynamic selection of ensembles of classifiers using contextual information
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Using diversity in classifier set selection for arabic handwritten recognition
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Dynamic zoning selection for handwritten character recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
An automatic method for construction of ensembles to time series prediction
International Journal of Hybrid Intelligent Systems
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Arabic handwriting word recognition is a challenging problem due to Arabic's connected letter forms, consonantal diacritics and rich morphology. One way to improve the recognition rates classification task is to improve the accuracy of individual classifiers; another, is to apply ensemble of classifiers methods. To select the best classifier set from a pool of classifiers, the classifier diversity is considered one of the most important properties in static classifier selection. However, the advantage of dynamic ensemble selection versus static classifier selection is that used classifier set depends critically on the test pattern. In this paper, we propose two approaches for Arabic handwriting recognition AHR based on static and dynamic ensembles of classifiers selection. The first one selects statically the best set of classifiers from a pool of classifier already designed based on diversity measures. The second one represents a new algorithm based on Dynamic Ensemble of Classifiers Selection using Local Reliability measure DECS-LR. It chooses the most confident ensemble of classifiers to label each test sample dynamically. Such a level of confidence is measured by calculating the proposed local reliability measure using confusion matrixes constructed during training level. We show experimentally that both approaches provide encouraging results with the second one leading to a better recognition rate for AHR system using IFN_ENIT database.