Document Images Retrieval Based on Multiple Features Combination

Authors:
G. Meng;N. Zheng;Y. Song;Y. Zhang
Affiliations:
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China;Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China;Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China;Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
Venue:
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Year:
2007

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Abstract

Retrieving the relevant document images from a great number of digitized pages with different kinds of artificial variations and documents quality deteriorations caused by scanning and printing is a meaningful and challenging problem. We attempt to deal with this problem by combining up multiple different kinds of document features in a hybrid way. Firstly, two new kinds of document image features based on the projection histograms and crossings number histograms of an image are proposed. Secondly, the proposed two features, together with density distribution feature and local binary pattern feature, are combined in a multistage structure to develop a novel document image retrieval system. Experimental results show that the proposed novel system is very efficient and robust for retrieving different kinds of document images, even if some of them are severely degraded.