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Texture sparseness for pixel classification of business document images

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Abstract

Contemporary business documents contain diverse, multi-layered mixtures of textual, graphical, and pictorial elements. Existing methods for document segmentation and classification do not handle well the complexity and variety of contents, geometric layout, and elemental shapes. This paper proposes a novel document image classification approach that distributes individual pixels into four fundamental classes (text, image, graphics, and background) through support vector machines. This approach uses a novel low-dimensional feature descriptor based on textural properties. The proposed feature vector is constructed by considering the sparseness of the document image responses to a filter bank on a multi-resolution and contextual basis. Qualitative and quantitative evaluations on business document images show the benefits of adopting a contextual and multi-resolution approach. The proposed approach achieves excellent results; it is able to handle varied contents and complex document layouts, without imposing any constraint or making assumptions about the shape and spatial arrangement of document elements.

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Notes

  1. http://web.uvic.ca/~mcote/BDID/.

  2. http://web.uvic.ca/~mcote/BDID/.

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Authors and Affiliations

  1. Electrical and Computer Engineering, University of Victoria, P.O. Box 3055, STN CSC, Victoria, BC, V8W 3P6, Canada

    Melissa Cote & Alexandra Branzan Albu

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  1. Melissa Cote
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  2. Alexandra Branzan Albu
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Correspondence to Alexandra Branzan Albu.

Additional information

This work was supported by the Natural Sciences and Engineering Research Council of Canada and SAP Canada through the Collaborative Research and Development Grants Program. Special thanks to Prof. Nicholas Journet for his help on implementing the comparison method of Sect. 5.4.

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Cote, M., Branzan Albu, A. Texture sparseness for pixel classification of business document images. IJDAR 17, 257–273 (2014). https://doi.org/10.1007/s10032-014-0217-8

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