Recognition of degraded character images

Pao Yue-kong Library Electronic Theses Database

Recognition of degraded character images

 

Author: Sun, Man-cheong
Title: Recognition of degraded character images
Degree: M.Sc.
Year: 1998
Subject: Optical character recognition devices
Optical data processing
Computer vision
Algorithms
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Electronic Engineering
Pages: 71, [5] leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1441883
URI: http://theses.lib.polyu.edu.hk/handle/200/3347
Abstract: Gray-level character images extracted from a natural scene are usually ambiguous because they are probably degraded by noise, inadequate resolution of sensor and blurring effect of lens system. Automatic recognition of these character images is therefore non-trivial. Two robust and reliable character recognition algorithms are proposed in this dissertation specially for recognizing this type of images. The proposed recognition systems base on mathematical morphology image processing algorithms. The first system performs grey-scale morphological operations to extract strokes from the character image. The features extracted are organised as a feature vector. This vector is then classified based on a weighted distance measure between the vector and the reference vectors. The second system applies the concept of fuzziness to characterise strokes and their spatial relations. Fuzzy attributed graphs (FAGs) are then used to represent the structural information of characters. These FAGs are compared with reference FAGs to accomplish the classification of character images. In the testing phase of the systems, 51 images of license plate are used. The total number of character images on these plates are 303. A character block segmentation algorithm is proposed to isolate these character images from the images of license plate. The algorithm makes use of the c-means clustering method to adaptively threshold the grey-scale image. In this test, both character recognition systems proposed can achieve 99.7% recognition rate.

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