1. UC Berkeley CS294-9 Fall 200011- 1 Document Image Analysis Lecture 20: Intro to Layout Richard J. Fateman Henry S. Baird University of California – Berkeley Xerox Palo Alto Research Center

2. UC Berkeley CS294-9 Fall 200011- 2 Page layout analysis Structural (Physical, Geometric) Layout Analysis Functional (Syntactic, Logical) Layout Analysis Read-order determination

3. UC Berkeley CS294-9 Fall 200011- 3 Structural Isolation of columns, paragraphs, lines words, tables, figures. Maybe letters. Without some layout analysis, much of the previous work would be impossible! Without layout analysis, what is the sequence of words in a multi-column format?

4. UC Berkeley CS294-9 Fall 200011- 4 Functional Typically domain dependent May require merging or splitting of syntactic components Encoding into ODA (object oriented document architecture) or SGML (DTD describes components like section, title..)

5. UC Berkeley CS294-9 Fall 200011- 5 Functional Components First page of a technical article may have Title Author Abstract, body/column1 body/column2 footnotes Pagination Journal name/volume/date… Business letter might have Sender Date Logo Recipient Body Signature

6. UC Berkeley CS294-9 Fall 200011- 6 Finding structural blocks

7. UC Berkeley CS294-9 Fall 200011- 7 Common Approaches Top Down analysis –Horizontal and vertical profiles –Recursive: columns, paragraphs/lines/words –As illustrated earlier Bottom Up analysis –Use adjacency based on Pixels / morphology of dilation (millions) RLE/ merge lines (thousands) Connected Components (hundreds) Look at the background (shape-directed covers) Also, human hints.

8. UC Berkeley CS294-9 Fall 200011- 8 Standard images…the Scanned Input

9. UC Berkeley CS294-9 Fall 200011- 9 Smear character boxes

10. UC Berkeley CS294-9 Fall 200011- 10 Smear words to get lines

11. UC Berkeley CS294-9 Fall 200011- 11 Smear lines to get paragraphs

12. UC Berkeley CS294-9 Fall 200011- 12 Issues: Sensitivity to noise. Solutions: –Clean up via kfill or similar filtering, ruthlessly –Divide the page (artificially) and keep the noise from affecting the document globally Slanted lines. Solution(s): –Deskew (since it is not too hard(?)) –Use nearest neighbors “docstrum” Concave regions (text flow around a box). Solution(?) look at background Variation in font, spacing can throw off analysis –Allow for local analysis

13. UC Berkeley CS294-9 Fall 200011- 13 Interactive semi-automatic zoning (RJF)

14. UC Berkeley CS294-9 Fall 200011- 14 Zoom in

15. UC Berkeley CS294-9 Fall 200011- 15 Scroll around

16. UC Berkeley CS294-9 Fall 200011- 16 View individual pixels

17. UC Berkeley CS294-9 Fall 200011- 17 Semi… Turn up the noise filter until we start to kill some of the punctuation. How? As we turn up the threshold, the number of connected components drops, then reaches a stable plateau after the noise is gone, and then drops again as we remove punctuation, the dots above the “i” etc.

18. UC Berkeley CS294-9 Fall 200011- 18 auto… Turn the horizontal smear knob until the number of components drops suddenly from about 3000 to about 600. Character boxes have been merged into wordboxes Turn the horizontal smear knob until the number of components drop from about 600 to about 100. Wordboxes have become lineboxes.

19. UC Berkeley CS294-9 Fall 200011- 19 matic.. Tweek the vertical smear knob. Lines become paragraphs. (Turn further, and paragraphs become columns).

20. UC Berkeley CS294-9 Fall 200011- 20 Specify read order

21. UC Berkeley CS294-9 Fall 200011- 21 Interactive functional tagging: mark subject/author/etc? Here we attempt automatic id of math… Automatic math zone. This is a challenge because the zone is in two parts, containing the math … f(p)=F(p)

22. UC Berkeley CS294-9 Fall 200011- 22 Docstrum/ L.O’Gorman 5 nearest neighbors (ogorman93)

23. UC Berkeley CS294-9 Fall 200011- 23 Example of “spectrum” Each point represents distance and angle of a cc. N^2, but not so bad.

24. UC Berkeley CS294-9 Fall 200011- 24 Statistics for skew and spacing

25. UC Berkeley CS294-9 Fall 200011- 25 Extract Lines, group to paragraphs Statistically close enough horizontally to be words, then lines Statistically close enough and parallel enough and the same length as… group two lines into the same text block. (arguably saving time by not deskewing; dealing with non-constant skew) Example follows..

26. UC Berkeley CS294-9 Fall 200011- 26 Sections with different skew 6 business cards, nearest neighbors vectors

27. UC Berkeley CS294-9 Fall 200011- 27 Extracted text lines, blocks Useful? General?

28. UC Berkeley CS294-9 Fall 200011- 28 Without layout analysis Reading across columns Misplacing captions Misplacing footnotes Misunderstanding page numbers (which should be REMOVED in the reformatting process) Need extraction of biblio data: title, author, abstract, keywords Nearly every subsequent step is compromised by lack of context.

29. UC Berkeley CS294-9 Fall 200011- 29 A Diversion: Separating Math from Text Why separate math from text? Types of mathematics encountered Previous Work Two approaches –post-processing commercial OCR –character-based (details!) Errors and their correction Ambiguities

30. UC Berkeley CS294-9 Fall 200011- 30 Why separate math/text/images/.. OCR programs do not work for math becomes, in Textbridge, Designation as a “picture” is only a partial solution

31. UC Berkeley CS294-9 Fall 200011- 31 Mathematics on a Page Inline is harder to pick out because it may look like italics text

32. UC Berkeley CS294-9 Fall 200011- 32 Previous Work Isolation by hand (most math parser papers) Texture/ statistics based heuristics –useful for display math “paragraphs” –not useful for in-line math Character based pseudo-parsing (but without font information or true parsing feedback) Incomplete

33. UC Berkeley CS294-9 Fall 200011- 33 Proposal: Post-Processing of OCR Start with commercial best-effort recognition Reprocess the intermediate data structure (e.g. for TextBridge, the XDOC file) Accept recognition of text zones with high recognition certainty. (Lines with no errors surrounded by lines with no errors are considered solved)

34. UC Berkeley CS294-9 Fall 200011- 34 Separate uncertain areas Re-consider “the rest of the image” as potential mathematics zones: uncertain regions (including nearby “certain” characters/lines) Isolate characters, identify fonts, etc. Play out heuristic rules for separating text and math zones. Consider eradicating math and re-submitting text; separately recognizing math and reinserting in XDOC

35. UC Berkeley CS294-9 Fall 200011- 35 Alternatively, Starting from our own naïve OCR Connected component recognition Separate characters by initial classification Repeatedly re-examine via rules Determine text zones, remove math / feed remainder to commercial OCR –How best to blank-out math? XXX Most likely human interaction remains

36. UC Berkeley CS294-9 Fall 200011- 36 Two bags: Math vs Text Initially MathInitially Math –+ - = / Greek, scientific symbols, 0-9, italics, bold, (), [], sin, cos, tan, dots, commas, decimal points Initially TextInitially Text –Roman Letters, junk

37. UC Berkeley CS294-9 Fall 200011- 37 Sample Text Bag

38. UC Berkeley CS294-9 Fall 200011- 38 Sample Math Bag

39. UC Berkeley CS294-9 Fall 200011- 39 Second Pass Correct for too much Math Grow “clumps” (expand BBs) to categorize –3.14159 vs “end of sentence.” –(comment) vs f(x) –hyphen-words vs x 2 - y 2 –horizontal lines generally –isolated 1 or is it l “ell” or I “eye” “bags” or “zones” of geometric-relation boxes containing either words or potential math

40. UC Berkeley CS294-9 Fall 200011- 40 Importance of Context Here are 12 L’s and a 1

41. UC Berkeley CS294-9 Fall 200011- 41 Third Pass Too much is in the text bag now –blur the math to allow for embedded Roman text like “sin” or “l” Re-clump the mathematics to see if new bridges have been formed Some italics in the math bag may be really –English words in theorems –emphasized text

42. UC Berkeley CS294-9 Fall 200011- 42 On Ambiguity and Correctness Can we find the math in ad - bc by ad hoc methods? If we are unable to disambiguate English words, why should we be able to disambiguate mathematics? Abuse of mathematical notation is widespread: can we insist that new papers either have a non-ambiguous notation or an underlying electronic non-ambiguous notation?

43. UC Berkeley CS294-9 Fall 200011- 43 Conclusions We can make a first cut on separating math from text If we wish to “enliven” math publication with semantic underpinnings, this may help in their production Incorporation of AI rule-based transformations as well as hand correction are likely to be important