Isolated Handwritten Vietnamese Character Recognition with Feature Extraction and Classifier Combination

Handwritten text recognition is a difficult problem in the field of pattern recognition. This paper focuses on two aspects of the work on recognizing isolated handwritten Vietnamese characters, including feature extraction and classifier combination. For the first task, based on the work in [1] we will present how to extract features for Vietnamese characters based on gradient, structural, and concavity characteristics of optical character images. For the second task, we first develop a general framework of classifier combination under the context of optical character recognition. Some combination rules are then derived, based on the Naive Bayesian inference and the Ordered Weighted Aggregating (OWA) operators. The experiments for all the proposed models are conducted on the 6194 patterns of handwritten character images. Experimental results will show the effective approach (with the error rate is about 4%) for recognizing isolated handwritten Vietnamese characters.