Jesper Zedlitz (German Society for Computer Genealogy, DE): Innovative Technologies for Making Crowdsourcing most Effective in Online Indexing co:op-READ-Convention Marburg Technology meets Scholarship, or how Handwritten Text Recognition will Revolutionize Access to Archival Collections. With a special focus on biographical data in archives Hessian State Archives Marburg Friedrichsplatz 15, D - 35037 Marburg 19-21 January 2016

1. Innovative Technologies for Making
Crowdsourcing most Effective in
Online Indexing
Dr. Jesper Zedlitz
Christian-Albrechts-Universität Kiel
Verein für Computergenealogie = German Society for Computer Genealogy

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Agenda
● Short introduction
● Examples
● “Data Entry System” (DES)
● Transcription Speed
● Transcription Quality
● Future development

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Short Introduction
● Verein für Computergenealogie
= German Society for Computer Genealogy
– since 1989
– largest German Genealogical Society
– 3.600 members
– no public funding

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Examples

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Examples
Many quite simple
entries on one page

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Examples
One or a few complex
entries on one page

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DES – The Project
● DES = Data Entry System
● objective: transcription of structured data
from printed sources
Family name: Kühnel
Birth name: Ziltz
Given names: Hertha Antonia
Date: 22.2.
Record number: 100

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DES – The Project
● start beginning of 2012
● first project: German WW1 casualty lists
– 31,000 pages

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DES – The Project
● meanwhile variety of other sources
– city directories,
– civil records,
– war grave lists, …

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DES – Technology
● web-based solution
+ no DVD shipping necessary
+ does not require special software
+ quick assistance possible
● work “on the image“
● search during work
+ voluntary transcribers want to see “their” entries online
● two-tiered transcription
1. single entries
2. groups of entries

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DES Web Application

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Web App – Data Entry

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Web App – Data Entry

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Web App – Data Entry

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Web App – Data Entry

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Web App – Search

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Web App – Search

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Web App – Search

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Web App – Area Data

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Web App – Area Data

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Web App – Area Data

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Web App – Area Data

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Web App – Area Data

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Web App – Area Data

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Does it work?

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Transcription Speed
● Transcribed entries and number of registered users

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Transcription Speed
● Entries per day

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How good is it?

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Approaches
● single transcription
● proofreading
transcriber
proofreader

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Approaches
● single transcription + double proofreading
– e.g. Wiki-Source
● proofreading with arbitration
expert

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Approaches
● double-keying with arbitration
● double-keying with N-keyed run-off votes
A B A, B or C
…
A B C A

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Approaches
● totally inefficient?
…
A B C A
? →

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Quality Improvement
● German WW1 casualty lists
– Gothic print
● small sample of 5,000 entries
● on average 15.6% errors for single keying
● varies from 6% to 78% for different users

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Quality Improvement
15.6% difference
1.1% difference
only 2.4% errors spotted
85% of the error
remained undetected!

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Future Development

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Future Development
● Entry of relations between entries

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Future Development
● Entry of relations between entries
● Data Entry on mobile devices

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CompGen · CAU Kiel
Future Development
● Entry of relations between entries
● Data Entry on mobile devices
● Captchas

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CompGen · CAU Kiel
Future Development
● Entry of relations between entries
● Data Entry on mobile devices
● Captchas
● Combination OCR + manual transcription

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Thank you for your attention!
Questions?
des.genealogy.net