Computer Science Research forFamily History and Genealogy

David W. Embley Heath Nielson, Mike Rimer, Luke Hutchison, Ken Tubbs, Doug Kennard, Tom FinniganWilliam A. Barrett

Computer Graphics, Vision, & Image Processing LaboratoryNeural Networks and Machine Learning Laboratory

Data Extraction and Integration LaboratoryLaboratory for Information, Collaboration, & Interaction Environments

Performance Evaluation LaboratoryData and Software Engineering Laboratory

www.cs.byu.edu/familyhistory

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?

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.

ZoningGeneral 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.

Zone ClassificationMachine vs. Handwriting

• Machine printed text is consistent/regular.

• Handwriting is irregular.

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.

Document Templates

Zoned Image

Automated Text Recognition

Word Segmentation

Letter Segmentation

Optical Character Recognition

Handwriting Recognition

Handwriting Recognition• 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

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

Handwriting Recognition

• An example of some steps in the analysis process:

– Contour extraction

– Midline determination

– Stroke ordering

Handwriting Recognition

• An example of some steps in the recognition process:

– Handwriting style clustering

– Letter recognition

– Approximate string matching

nr?

m?SmithSmythe

Automatic Record ExtractionAutomatic Record Extraction

Extraction AlgorithmExtraction Algorithm

1. Identify the Geometric Structure

2. Identify the Type of Information

3. Identify the Attribute-Value pairs

4. Identify the Record Boundaries

Column-Row RecognitionColumn-Row Recognition

Genealogical OntologyGenealogical Ontology

ROAD, STREET, &c.,ROAD, STREET, &c.,And No. or NAME of HOUSEAnd No. or NAME of HOUSE

Match LabelsMatch LabelsLocationLocation

Match LabelsMatch LabelsNAME and Surname of NAME and Surname of each Personeach PersonFull NameFull Name LocationLocation

RELATION to Head RELATION to Head of Family of FamilyRelationshipRelationship

Match LabelsMatch LabelsLocationLocation Full NameFull Name

Extract RecordsExtract Records

CollaferCollaferLocationLocation Full NameFull Name RelationshipRelationship

Extract RecordsExtract Records

John EyresJohn Eyres HeadHeadLocationLocation Full NameFull Name RelationshipRelationship CollaferCollafer

Extract RecordsExtract Records

Annie EyresAnnie Eyres WifeWifeLocationLocation Full NameFull Name RelationshipRelationship CollaferCollafer

Extract RecordsExtract Records

Lehailes EyresLehailes Eyres SonSonLocationLocation Full NameFull Name RelationshipRelationship CollaferCollafer

John

Web QueryWeb Query

Eyres

Search ResultsSearch Results

Online Digital Microfilm: ProblemMany of the images we are interested in are quite large.

6048 x 4287 pixels

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:

Hierarchical PITSequential Transmission(Progressive Image Transmission)

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)

Digital Microfilm Browser

PAF – 5 Generation Pedigree

PAF – 5 Generation Pedigree

Gena:A 3D Genealogy Visualizer

Concluding Remarks

Workshop: April 4, 2002 at BYUwww.cs.byu.edu/familyhistory

AppendixCategorized List of BYU Faculty Interests in

Computer Science Research Topicsthat Support Technology for

Family History and Genealogy

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)

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)

Information Integration

• Definition of Ontological Expectations (Embley, Woodfield)

• Value Normalization (Woodfield)• Object Identity & Data Merging (Embley, Sederberg)• Managing Uncertainty (Embley, Woodfield,

Martinez)

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)