Cut digits classification with k-NN multi-specialist

Authors:
Fernando Boto;Andoni Cortés;Clemente Rodríguez
Affiliations:
Computer Architecture and Technology Department, Computer Science Faculty, UPV/EHU, San Sebastian, Spain;Computer Architecture and Technology Department, Computer Science Faculty, UPV/EHU, San Sebastian, Spain;Computer Architecture and Technology Department, Computer Science Faculty, UPV/EHU, San Sebastian, Spain
Venue:
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Year:
2006

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Abstract

A multi-classifier formed by specialised classifiers for noise produced by an image is shown in this work. A study has been carried out in the case of cut images, where tree cases of specialization are considered. Classifiers based on neighbourhood criteria are used, the zoning global feature and the Euclidean distance too. Furthermore, the paper explains a modification of the Euclidean distance for classifying cut digits. The experiments have been carried out with images of typewritten digits, taken from real forms. Trying to obtain a strong database to support the experiments, we have cut images deliberately. The recognition rate improves from 84.6% to 97.70%, but whether the system provides information about the disturbance of the image, it can achieve a 98.45%.