On the representation of multi-agent aggregation using fuzzy logic
Cybernetics and Systems
Recognition of handwritten digits using template and model matching
Pattern Recognition
A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting meaningful handwriting features with fuzzy aggregation method
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Online Handwritten Script Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Fuzzy Geometric Representation for On-Line Isolated Character Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Character Recognition Systems: A Guide for Students and Practitioners
Character Recognition Systems: A Guide for Students and Practitioners
Fuzzy Sets and Systems
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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This paper describes two novel methodologies for recognition of old Slavic Cyrillic characters. The first recognition method is based on a decision tree classifier and the second one uses a fuzzy classifier. Both methods use the same set of features extracted from the character bitmaps. The prototypes are obtained by applying the logical operators on the samples of digitalised characters from original manuscripts. According to the experimental results relevant features for defining a particular character are number and position of spots in the outer segments, presence and position of horizontal and vertical lines and holes, compactness and symmetry. The fuzzy classifier creates a prototype which consists of fuzzy rules by means of fuzzy aggregation of character features. The classifier based on a decision tree is realised by a set of rules. We have implemented the proposed classifiers and have experimentally tested their efficiency calculating their recognition accuracy and precision.