Authors:
Olarik Surinta
;
Lambert Schomaker
and
Marco Wiering
Affiliation:
University of Groningen, Netherlands
Keyword(s):
Handwritten character recognition, Feature extraction, \emph{k}-Nearest neighbors, classification.
Related
Ontology
Subjects/Areas/Topics:
Classification
;
Feature Selection and Extraction
;
Instance-Based Learning
;
Pattern Recognition
;
Theory and Methods
Abstract:
Feature extraction techniques can be important in character recognition, because they can enhance the efficacy of recognition in comparison to featureless or pixel-based approaches. This study aims to investigate the novel feature extraction technique called the hotspot technique in order to use it for representing handwritten characters and digits. In the hotspot technique, the distance values between the closest black pixels and the hotspots in each direction are used as representation for a character. The hotspot technique is applied to three data sets including Thai handwritten characters (65 classes), Bangla numeric (10 classes), and MNIST (10 classes). The hotspot technique consists of two parameters including the number of hotspots and the number of chain code directions. The data sets are then classified by the k-Nearest Neighbors algorithm using the Euclidean distance as function for computing distances between data points. In this study, the classification rates obtained fr
om the hotspot, mark direction, and direction of chain code techniques are compared. The results revealed that the hotspot technique provides the largest average classification rates.
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