Large Tree Classifier with Heuristic Search and Global Training
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Iterative Growing and Pruning Algorithm for Classification Tree Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Form Design for High Accuracy Optical Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Feature Generation for Handwritten Digit Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Joint Induction of Shape Features and Tree Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
A structural/statistical feature based vector for handwritten character recognition
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Neural-network classifiers for recognizing totally unconstrained handwritten numerals
IEEE Transactions on Neural Networks
Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM)
IEEE Transactions on Neural Networks
An accelerated learning algorithm for multilayer perceptrons: optimization layer by layer
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An efficient constrained training algorithm for feedforward networks
IEEE Transactions on Neural Networks
Handwritten numeral recognition based on simplified structural classification and fuzzy memberships
Expert Systems with Applications: An International Journal
Handwriting Recognition Algorithm in Different Languages: Survey
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
A hybrid method for robust car plate character recognition
Engineering Applications of Artificial Intelligence
A neuro-fuzzy inference engine for Farsi numeral characters recognition
Expert Systems with Applications: An International Journal
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Feature representation selection based on Classifier Projection Space and Oracle analysis
Expert Systems with Applications: An International Journal
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A hybrid classification system with neural network and decision tree as the classifiers for handwritten numeral recognition is proposed. Firstly a variety of stable and reliable global features are defined and extracted based on the character geometric structures, a novel floating detector is then proposed to detect segments along the left and right profiles of a character image used as local features. The recognition system consists of a hierarchical coarse classification and fine classification. For the coarse classifier: a three-layer feed forward neural network with back propagation learning algorithm is employed to distinguish six subsets {0}, {6}, {8}, {1,7}, {2, 3, 5}, {4, 9} based on the feature similarity of characters extracted. Three character classes namely {0}, {6} and {8} are directly recognized from artificial neural network (ANN). For each of characters in the latter three subsets, a decision tree classifier is built for further fine classification as follows: Firstly, the specific feature-class relationship is heuristically and empirically deduced between the feature primitives and corresponding semantic class. Then, an iterative growing and pruning algorithm is used to form a tree classifier. Experiments demonstrated that the proposed recognition system is robust and flexible and a high recognition rate is reported.