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Laserfiche Clinic 2006-2007

Laserfiche Clinic 2006-2007. Adam Field. Ben Tribelhorn, PM. Advisor:. Zach Dodds. Aaron Wolin. Stephen Smith. Liaison Luncheon @ HMC, Sept. 12 th , 2006. The Problem. raw image. OCR-able image.

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Laserfiche Clinic 2006-2007

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  1. Laserfiche Clinic 2006-2007 Adam Field Ben Tribelhorn, PM Advisor: Zach Dodds Aaron Wolin Stephen Smith Liaison Luncheon @ HMC, Sept. 12th, 2006

  2. The Problem raw image OCR-able image To convert pictures of documents taken with a digital camera into images that can be organized using Laserfiche's OCR and database technologies. Project goal:

  3. The Problem raw image OCR-able image To convert pictures of documents taken with a digital camera into images that can be organized using Laserfiche's OCR and database technologies. Project goal: • presence of paperclips and/or staples • varied/confusing backgrounds (including stacks of papers) • one or more edges off the edge of the image • knowing when the system has failed • camera perspective issues - documents not images head-on (?) • other important cases? Some important cases:

  4. Approaches Outside - In Inside - Out ? • Approach taken by previous clinic • Finding document corners • Unwarping to 8.5 x 11" • Possible approach taken by current clinic • First analyzing text-line boundaries • Then unwarping to straighten them

  5. Camera Document Restoration for OCR • Able to detect the type of distortion or severity of the warping • Uses “Vertical Stroke Boundaries” VSBs of characters VSBs • Several algorithms use VSBs to detect and correct the image Lu and Tan. “Camera Document Restoration for OCR.” http://www.m.cs.osakafu-u.ac.jp/cbdar/proceedings/papers/O1-3.pdf

  6. Finding Vertical Stroke Boundaries • Connected components first • Find the "top" and "base" lines for a line of text • Scan between the top and base lines, searching for pixels that form relatively orthogonal and straight lines Tip point tracing process. Lu, Chen, and Ko. “Perspective rectification of document images using fuzzy set and morphological operations.” http://vlab.ee.nus.edu.sg/~bmchen/papers/ivc.pdf

  7. A Fast Orientation and Skew Detection Algorithm • Uses connected components and nearest neighbors to find document skew • Places the text line angles into two histograms from ±90º Precisions are 1.0º and 0.1º • The skew angle is the histogram peak Avila and Lins. “A Fast Orientation and Skew Detection Algorithm for Monochromatic Document Images.” http://delivery.acm.org/10.1145/1100000/1096631/p118-avila.pdf

  8. Problem Taxonomy Hand-writing Magazines/Newspaper document difficulty Forms Mostly text documents Geometric Skew Perspective warp severity

  9. Problem Priorities ? Hand-writing Magazines/Newspaper secondary focus document difficulty Forms Mostly text documents Geometric Skew Perspective primary focus warp severity

  10. Pair 1's plan Finding character strokes Estimating warp severity Thresholding picture from ben and stephen

  11. Pair 2's plan Least-sq. line-fitting Visualizing the processing Finding skew estimates Two-tier assessment 1) reasonable? 2) OCR accuracy picture from aaron & adam

  12. Tentative Schedule Th 9/21 (11:30 am) Call - progress update T 9/26 Initial presentation @ Harvey Mudd Th 9/28 Prototype of each algorithm F 10/6? Site visit and presentation @ Laserfiche Weekly conference calls with Ed Heaney Accessible codebase and performance updates Other deliverables ?

  13. Comments?

  14. Other Papers

  15. Hand Writing Magazines Forms Plain Text Skew Perspective Geometric Image Warping

  16. Taxonomy Hand-writing Magazines/Newspaper Forms Mostly text documents Geometric Skew Perspective

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