DocumentCode :
2399728
Title :
Multi-font and multi-size character recognition based on the sampling and quantization of an unwrapped contour
Author :
Kim, Min-Ki ; Kwon, Young-Bin
Author_Institution :
Dept. of Comput. Sci. & Eng., Chungang Univ., Seoul, South Korea
Volume :
3
fYear :
1996
fDate :
25-29 Aug 1996
Firstpage :
170
Abstract :
We propose a character recognition method based on the sampling and quantization of unwrapped contour information. The character contour includes many different features. Numerous approaches are explored to find essential features which are extracted directly from the contour. Our idea is to decompose the multi-dimensional features of a contour into one dimensional features. Following the contour, we extract horizontal, vertical, and angular variations and make unwrapped contours from these features. Unwrapped contour information is sensitive to the starting point of contour tracing, and it varies on the font type and size, so we propose a novel algorithm which finds the invariant starting point of contour tracing. By sampling and quantization, we can make discrete contours which are invariant to the font and size
Keywords :
feature extraction; image classification; image sampling; optical character recognition; quantisation (signal); contour tracing; discrete contours; multi-dimensional features; multi-font multi-size character recognition; quantization; sampling; unwrapped contour; Character recognition; Computer science; Computer vision; Data mining; Feature extraction; Optical character recognition software; Pattern recognition; Quantization; Sampling methods; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location :
Vienna
ISSN :
1051-4651
Print_ISBN :
0-8186-7282-X
Type :
conf
DOI :
10.1109/ICPR.1996.546816
Filename :
546816
Link To Document :
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