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Document Image Analysis

  • Chapter
Automatic Digital Document Processing and Management

Part of the book series: Advances in Pattern Recognition ((ACVPR))

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Abstract

One of the main distinguishing features of a document is its layout, as determined by the organization of, and reciprocal relationships among, the single components that make it up. For many tasks, one can afford to work at the level of single pages, since the various pages in multi-page documents are usually sufficiently unrelated to be processed separately. This chapter discusses the processing steps that lead from the original document to the identification of its class and of the role played by its single components according to their geometrical aspect: digitization (if any), low-level pre-processing for documents in the form of images or expressed in term of very elementary layout components, optical character recognition, layout analysis and document image understanding. This results in two distinct but related structures for a document (the layout and the logical one), for which suitable representation techniques are introduced as well.

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Notes

  1. 1.

    A binary relationship is an ordering relationship if it is reflexive, antisymmetric and transitive.

  2. 2.

    An evolution of this model that considers also the complement of the region (and hence a 3×3 matrix), called 9-Intersection model, was successively developed [16, 18].

  3. 3.

    Another model, called SAX (Simple API for XML), processes the documents linewise. This avoids the need to completely load them into memory, but bounds processing to proceed forward only (once an item is passed over, it can be accessed again only by restarting processing from the beginning).

  4. 4.

    A minimum resolution for allowing significant processing is 300 dpi, but thanks to the recently improved performance of computers the current digitization standards are becoming 400 dpi, with a trend towards 600 dpi.

  5. 5.

    Switching from the color space to a logic one, they can be interpreted as denoting a pixel being important, in terms of True or False.

  6. 6.

    Code available at http://code.google.com/p/tesseract-ocr/.

  7. 7.

    The source code of Tesseract is structured in several directories:

    ccmain :

    main program

    training :

    training functionalities

    display :

    a utility to view and operate on the internal structures

    testing :

    test scripts (also contains execution results and errors)

    wordrec :

    lexical recognition

    textord :

    organization of text in words and lines

    classify :

    character recognition

    ccstruct :

    structures for representing page information

    viewer :

    client-side interface for viewing the system (no server side is yet available)

    image :

    images and image processing functionalities

    dict :

    language models (including extension by addition of new models)

    cutil :

    management of file I/O and data structures in C

    ccutil :

    C++ code for dynamic memory allocation and data structures.

  8. 8.

    Code available at http://vietocr.sourceforge.net/.

  9. 9.

    Version 0.9.2 of JTOCR, the latest available at the time of writing, has a bug in the crop operation (method jMenuItemOCRActionPerformed of class gui.java). Let us represent a rectangle having top-left corner at coordinates (x,y), width w and height h as a 4-tuple (x,y,w,h). Given a selection (x S ,y S ,w S ,h S ) of the original image at zoom ratio p, the excerpt is identified as \((\frac{x_{S}}{p},\frac{y_{S}}{p},\frac {w_{S}}{p},\frac{h_{S}}{p})\). Clearly, no displacement is taken into account. To fix the problem, the offset \((x_{o},y_{o}) = (\frac {x_{P}-x_{I}}{2},\frac{y_{P}-y_{I}}{2})\) between the container panel (x P ,y P ,w P ,h P ) and the image (x I ,y I ,w I ,h I ) has to be considered, and hence the correct formula is \((\frac{x_{S}-x_{o}}{p},\frac {y_{S}-y_{o}}{p},\frac{w_{S}}{p},\frac{h_{S}}{p})\).

  10. 10.

    A tree where the offspring of a node can be partitioned into groups such that the elements in the same group are considered in AND and groups are considered in OR.

  11. 11.

    A tree built from a graph using all of its nodes and just a subset of its edges (spanning tree) such that the sum of weights of the subset of edges chosen to make up the tree is minimum with respect to all possible such trees. In Kruskal’s algorithm [48], it is built by progressively adding the next unused edge with minimum weight, skipping those that yield cycles, until a tree is obtained.

  12. 12.

    A simplified profile of the ISO 8601 standard, that combines in different patterns groups of digits YYYY, MM, DD, HH, MM, SS expressing year, month, day, hour, minute and second, respectively.

  13. 13.

    Internet Media Type, formerly MIME types.

  14. 14.

    The typical two-character language codes, optionally followed by a two-character country code (e.g., it for Italian, en-uk for English used in the United Kingdom).

  15. 15.

    Thesaurus of Geographic Names.

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  1. Dipartimento di Informatica, Università di Bari, Via E. Orabona 4, 70126, Bari, Italy

    Stefano Ferilli

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  1. Stefano Ferilli
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Ferilli, S. (2011). Document Image Analysis. In: Automatic Digital Document Processing and Management. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-198-1_5

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  • DOI: https://doi.org/10.1007/978-0-85729-198-1_5

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