Artificial neural systems: foundations, paradigms, applications, and implementations
Artificial neural systems: foundations, paradigms, applications, and implementations
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Structure Extraction from Decorated Characters Using Multiscale Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical and Experimental Analysis of a Two-Stage System for Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Confidence-based dynamic ensemble for image annotation and semantics discovery
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Segmentation of Low-Quality Typewritten Digits
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Semantics and feature discovery via confidence-based ensemble
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
Using natural class hierarchies in multi-class visual classification
Pattern Recognition
Noisy digit classification with multiple specialist
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Cut digits classification with k-NN multi-specialist
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Towards improving automatic image annotation using improvised fractal SMOTE approach
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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A classifier for an automatic system that recognizes multi font typewritten digits, often broken and blurred, in forms is presented. The classification, which is based on the utilization of a global feature, is applied in two phases. Firstly, a minimum distance method (1-NN) is applied in a multifont classifier to provide a global classification of the patterns in a form. A problem associated to multifont classifiers is the interference among classes in different fonts. An interesting aspect of this particular application is that it is highly probable that a form includes just one font. Then, in the second phase, a specialized classifier, oriented to one-form, uses the patterns in the form previously classified to validate, or reject and reclassify them, on the basis of the mean distance to the predefined classes. This specialized classifier affords significant improvement in performance. A classification accuracy rate of 99.42% has been achieved.