Document Image Retrieval Based on Texture Features: A Recognition-Free Approach
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The tendency of current technology is towards a paperless world. Due to the rapid increase of digitized documents, providing a fast and easy method for retrieval is in high demand. The aim of this paper is to examine the effectiveness of texture features for document image retrieval. Thus, segmentation-free document image retrieval using a binary texture method is proposed. In the proposed approach, local features are extracted, local grey-level structures are summarised, and their distribution is characterised using global features. The assumption is that texture properties in the text regions and non-text regions of the document images are different. This assumption is used to rank the available document images and retrieve only those, which have greatest visual similarity to a given query. The under-sampled image and sub-images of the original image are further considered to improve the retrieval results, which are up to 76.0% in the first ranking and 96.2% in the Top-10 ranking. The Media Team Oulu Document Database, which is a heterogeneous database that offers a great variety of page layouts and contents, is used for experimentation.
2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
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Pattern Recognition and Data Mining