tailieunhanh - Báo cáo " 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. . | VNU Journal of Science Mathematics - Physics 26 2010 123-139 Isolated Handwritten Vietnamese Character Recognition with Feature Extraction and Classifier Combination Le Anh Cuong Ngo Tien Dat Nguyen Viet Ha University of Engineering and Technology VNU E3-144 Xuan Thuy Cau Giay Hanoi Vietnam Received 5 July 2010 Abstract. 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. Keywords artificial intelligence optical character recognition classifier combination. 1. Introduction The problem handwriting recognition receives input as intelligible handwritten sources such as paper documents photographs touch-screens and other devices and try to output as correct as possible the text corresponding to the sources. The image of the written text may be sensed off-line from a piece of paper by optical scanning so actually it lies in the field of optical character recognition. Alternatively the movements of the pen tip may be sensed on-line for example by a penbased computer screen surface. Off-line handwriting recognition is generally observed to be harder than online handwriting