A Scanning n-tuple Classifier for Online Recognition of Handwritten Digits

Authors:
Eugene H. Ratzlaff
Affiliations:
-
Venue:
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Year:
2001

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Abstract

Abstract: A scanning n-tuple classifier is applied to the task of recognizing online handwritten isolated digits. Various aspects of preprocessing, feature extraction, training and application of the scanning n-tuple method are examined. These include: distortion transformations of training data, test data perturbations, variations in bitmap generation and scaling, chain code extraction and concatenation, various static and dynamic features, and scanning n-tuple combinations. Results are reported for both the UNIPEN Train-R01/V07 and DevTest-R01/V02 subset 1a isolated digits databases.