Document image retrieval based on density distribution feature and key block feature

Abstract

Document image retrieval is an important part of many document image processing systems such as paperless office systems, digital libraries and so on. Its task is to help users find out the most similar document images from a document image database. For developing a system of document image retrieval among different resolutions, different formats document images with hybrid characters of multiple languages, a new retrieval method based on document image density distribution features and key block features is proposed in this paper. Firstly, the density distribution and key block features of a document image are defined and extracted based on documents' print-core. Secondly, the candidate document images are attained based on the density distribution features. Thirdly, to improve reliability of the retrieval results, a confirmation procedure using key block features is applied to those candidates. Experimental results on a large scale document image database, which contains 10385 document images, show that the proposed method is efficient and robust to retrieve different kinds of document images in real time.