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Document Image Processing

Document Image Processing. Fourier Transforms Hough Transforms Docstrum Text vs Graphics. Features. Hamming Distance, HD = Correlation: Central Moments:. Spread= Slenderness= Fourier descriptors. Fourier Transform.

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Document Image Processing

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  1. Document Image Processing • Fourier Transforms • Hough Transforms • Docstrum • Text vs Graphics

  2. Features Hamming Distance, HD = Correlation: Central Moments: Spread= Slenderness= Fourier descriptors

  3. Fourier Transform

  4. Document Images and FT

  5. Hough Transform • Parametric Form • Global • Peaks in Accumulator Space • Y intercept is infinite • Use (r, )

  6. Accumulator array [r, ] r 180o 0o -r 0 0 90 180

  7. Document Skew • Adjust a binary image f(x,y) into portrait mode, if necessary. • For each row of the binary image f(x,y), generate and label its black runs. • Build objects based on black runs of the same labels and update extreme coordinates of objects. • Create a simplified binary image by preserving the last black runs of each "allowable" object. • Apply the Hough transform on the simplified binary image. • Analyze the local maxima of the Hough accumulator cell array to detect the skew angle of the binary image as follows: • (a) Collect the first and second maxima of Hough accumulator cell array elements. • (b) Collect all Hough accumulator cell array elements of which the values are greater than one-half of the second maxima of Hough accumulator cell element. • (c) Add these values together based on their angle. • (d) The skew angle is the angle corresponding to the maximum of these values. Document Skew Angle Detection Algorithm, D. X. Le and G. Thoma, Proc. 1993 SPIE Symposium on Aerospace and Remote Sensing -Visual Information Processing II, Orlando, FL, April 14-16, 1993, Vol. 1961, pp. 251-262.

  8. Physical Layout Structure • The description of the physical layout is constructed from the • information extracted from the document image during page • segmentation and classification • • information about the position, dimensions, shape etc. • • Geometrical relationships between components • • Eg., for a technical article may contain information such as: ‘text component with a set S1 of attributes below a line drawing component with a set S2 of attributes’ • – the Office Document Architecture (ODA) • – the Standard Generalized Markup Language (SGML) • – The eXtensible Markup Language (XML)

  9. Docstrum Slope Histograms Use local information Connect a mark (component) with K (=4..6) neighbors Histogram of the slopes More efficient than projection profiles Docstrum is the radius and angle plot of the slopes

  10. Extracting Text Strings • Set H to the average height of the marks being considered • Set the Hough space resolution in to 1o and r to 0.2H • Apply the Hough transform to all marks, using the ranges • Set the mark count threshold to T=20 • For each cell in the accumulator space with count greater than T • a) For the cluster of cells calculate the average height Hlocal of marks contributing to the cluster • b) Compute a new clustering factor f= Hlocal/R re-cluster cells • c) Perform string segmentation on marks contributing to new clusters • 6. Update Hough transform by deleting contributions from discarded components in the step 5c above • 7. Decrement T by 1, and if T>2 go to Step 5 • 8. Compute the Hough transform for the entire range of and go to step 4

  11. Physical Layout Analysis • • A document contains the information that its author wishes to convey • – By the formatting of characters and pictorial information and the general layout of the document • – The shape and size of paragraphs and illustrations, the font of the characters as well as their positions in the page can carry a message • • The physical layout denotes the organization of the text and graphical components in the document • • Physical layout analysis comprises of • page segmentation • page classification and • physical layout structure extraction

  12. Page Segmentation • • Page segmentation is the identification of areas of interest in the document image • – Identifies the boundaries of the areas in the image that correspond to the printed regions on the page • – A higher-level description of the page is obtained, in terms of the outlines (contours) of these areas • • Methods: • – Connected components aggregation (bottom-up) • – Projection-profiles analysis (top-down) • – Analysis of background space (hybrid)

  13. Page Classification Page classification is the determination of the type of the contents of each area of interest in the document image • Analyze attributes of the contents of each area and deduce its type – In OCR applications, one is interested in text and non-text – In Graphics applications the non-text areas must be assigned line drawing, halftone, photograph, etc. Texture analysis method: p(i,j) is formed representing the number of times the image contains a horizontal run of length j whose black and white proportion is in category i. Categories are made in bins i) less than 10%, (ii) 10-20%, etc.

  14. Logical Structure Semantic structure: Eg., Find abstracts of all papers in a database which include a keyword Physical structure of a newspaper: extraction of blocks of text, graphics, half-tones, identification of attributes such as fonts, size, style Logical structure is identifying headlines, captions, bylines, grouping paragraphs belonging to same story across columns, pages etc HTML vs XML :: Physical vs Logical

  15. Physical Layout Analysis (Lit Survey) Wahl et al: Closing with a horizontal kernel (300) ANDClosing with vertical kernel (30) Nagy and Seth: X-Y tree Fisher et al: Low resolution image used Lebourgeois et al: Non-uniform down sampling; Dilation by a horizontal kernel Bloomberg: Vertical dilation followed by a close-open sequence to remove noise, followed by a hit-or-miss transform to identify seed points of characters to identify italics and bold fonts Saitoh and Pavlidis: Non-uniform down sampling Hinds et al: Erosion using 2-pixel vertical (horizontal) kernel. Followed by Hough Transform Pavlidis and Zhou: Projection profile and clustering Amamoto et al: Open white space with long horizontal structural element followed by vertical and take union O’ Gorman: Docstrum Ishitani: document skew using line complexities to take care of non-text blocks Chen and Haralick: Recursive opening and closing Ankindele and Belaid: permit non-rectangular blocks

  16. Logical Layout Analysis (Lit Survey) Tsujimoto and Asada: Rule based system Fisher: Rule based system Chenvoy and Belaid: Blackboard system Kreich et al: Top-down knowledge based system Derrien-Peden: Frame-based system Yamashita et al: Model-based method Dengel: Busines letters

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