DocumentCode :
594799
Title :
Structured document classification by matching local salient features
Author :
Siyuan Chen ; Yuan He ; Jun Sun ; Naoi, Satoshi
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
653
Lastpage :
656
Abstract :
Following the recent trend in using low level image features in classifying document images, in this paper we present a novel approach for structured document classification by matching the salient feature points between the query image and the reference images. Our method is robust to diverse training data size, image formats and qualities. Through matching the feature points, image registration is available for the query image as well. Although we aimed for the large domain of the structured document images, our method already achieved zero error rates in the tests on the benchmark NIST tax form databases.
Keywords :
document image processing; feature extraction; image classification; image matching; image registration; query processing; visual databases; benchmark NIST tax; image formats; image qualities; image registration; local salient feature point matching; low level image features; query image; reference images; structured document image classification; training data size; zero error rates; Accuracy; Databases; Layout; NIST; Text analysis; Training; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460219
Link To Document :
بازگشت