1. Computer Science Research for Family History and Genealogy David W. Embley Heath Nielson, Mike Rimer, Luke Hutchison, Ken Tubbs, Doug Kennard, Tom Finnigan William A. Barrett Computer Graphics, Vision, & Image Processing Laboratory Neural Networks and Machine Learning Laboratory Data Extraction and Integration Laboratory Laboratory for Information, Collaboration, & Interaction Environments Performance Evaluation Laboratory Data and Software Engineering Laboratory www.cs.byu.edu/familyhistory

2. The Problem 2.5 million rolls of microfilm Assuming 1000 images per roll 2.5 billion images Is there a way to automatically extract this information?

3. A (Possible) Solution Input: Images of Microfilmed Records –Table Recognition (Heath Nielson) –Old-Text Recognition (Mike Rimer) –Handwriting Recognition (Luke Hutchison) –Record Extraction & Organization (Ken Tubbs) –Just-in-Time Browsing (Doug Kennard) –Visualization (Tom Finnigan) Output: Organized Genealogical Information Let a computer do the extraction work.

4. Zoning General Overview Find the lines in the document using the horizontal and vertical profiles of the image. Apply a matched filter to the profiles to identify the line signatures. Recursively divide the document into separate pieces, analyzing each piece for lines.

7. Zone Classification Machine vs. Handwriting Machine printed text is consistent/regular. Handwriting is irregular.

8. Document templates Images are not ideal. –Results in incorrect zoning and classification. Form layout is the same across documents. –Features missed in one image, are found in another. Build a template of the document’s form by using several documents. –Provides robustness, and increases accuracy.

9. Document Templates

10. Zoned Image

11. Automated Text Recognition

12. Word Segmentation

13. Letter Segmentation

14. Optical Character Recognition

15. Handwriting Recognition

16. The Task – Online handwriting recognition The writer's pen movements are captured Velocity, acceleration, stroke order are available – Offline handwriting recognition Page was previously-written and scanned Only pixel color information available Genealogical records are all offline Offline is harder, but doable Mary

17. Handwriting Recognition Can we just convert offline data into (simulated) online data? – Yes, although difficult to do reliably: What order were the strokes written in? Doubled-up line segments? Ink blobs? Spurious joins between letters? Missing joins? – Inferring online data (e.g. stroke ordering) could be crucial to success – Demonstrated to be solvable with reasonable reliability

18. Handwriting Recognition An example of some steps in the analysis process: –Contour extraction –Midline determination –Stroke ordering

19. Handwriting Recognition An example of some steps in the recognition process: –Handwriting style clustering –Letter recognition –Approximate string matching nr? m? Smith Smythe

20. Automatic Record Extraction

21. Extraction Algorithm 1.Identify the Geometric Structure 2.Identify the Type of Information 3.Identify the Attribute-Value pairs 4.Identify the Record Boundaries

22. Column-Row Recognition Column-Row Recognition

23. Genealogical Ontology

24. ROAD, STREET, &c., And No. or NAME of HOUSE Match Labels Location

25. NAME and Surname of each Person Full Name Location

26. RELATION to Head of Family Relationship Match Labels Location Full Name

27. Extract Records CollaferLocation Full Name Relationship

28. Extract Records John Eyres Head Location Full Name RelationshipCollafer

29. Extract Records Annie Eyres Wife Location Full Name RelationshipCollafer

30. Extract Records Lehailes Eyres Son Location Full Name RelationshipCollafer

31. John Web Query Eyres

32. Search Results

33. Online Digital Microfilm: Problem Many of the images we are interested in are quite large. 6048 x 4287 pixels

34. What is Just-In-Time Browsing? Progressive Image Transmission: Hierarchical Spatial Resolution Progressive Bitplane Encoding JBIG Compressed Bitplanes Prioritized Regions of Interest User Interaction A method of quickly browsing digital images over the Internet which capitalizes on:

35. Hierarchical PIT Sequential Transmission (Progressive Image Transmission)

36. PIT Using Bitplane Method 1 BitPlane (2 levels of gray) 2 BitPlanes (4 levels of gray) 3 BitPlanes (8 levels of gray) 4 BitPlanes (16 levels of gray)

37. Digital Microfilm Browser

38. PAF – 5 Generation Pedigree

40. Gena: A 3D Genealogy Visualizer

41. Concluding Remarks Workshop: April 4, 2002 at BYU www.cs.byu.edu/familyhistory

42. Appendix Categorized List of BYU Faculty Interests in Computer Science Research Topics that Support Technology for Family History and Genealogy

43. Extraction from Digitized Images Scanning (Flanagan) Segmentation & Table Recognition (Barrett, Martinez) OCR for Old Type-Set Text (Martinez) Element Classification & Record Construction (Embley, Barrett, Martinez) Handwriting Recognition (Sederberg) Recognition of Hand-printed Text (Olson, Barrett, Martinez)

44. Extraction from Digital Data Sources Automatic Extraction from Semi-structured and Unstructured Sources (Embley, Martinez) Mappings from Heterogeneous Structured Source Views to Target Views (Embley) Individualized Source Views (Woodfield)

45. Information Integration Definition of Ontological Expectations (Embley, Woodfield) Value Normalization (Woodfield) Object Identity & Data Merging (Embley, Sederberg) Managing Uncertainty (Embley, Woodfield, Martinez)

46. Systems for Family History and Genealogy Storage of Large Volumes of Data (Flanagan) Distributed Storage (Woodfield) Indexing Original Documents (Martinez, Embley) Human-Computer Interaction (Olsen) Just-in-Time Browsing (Barrett, Olsen) Workflow for Directing Genealogical Work (Woodfield, Martinez, Embley) Notification Systems (Woodfield) Visualization (Sederberg)