Slides of the workshop at dh summer school, University of Lausanne (dhsummerschool.ch).

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Project
Automatic Text Recognition
dh summer school Lausanne
Tobias Hodel (Staatsarchiv Zürich)
Staatsarchiv

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11.30-12.00 Introduction to text recognition (+ READ)
12.00-12.30 Use Transkribus
12.30-13.00 What's next for text recognition/ feedback
Program of workshop

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- Recognition of layout and text structures
- Recognition of writing
- Implementation of dictionaries
- Type/Writer identification
Perspectives for institutions
- Best-practices for recognition of large amounts of documents
Perspectives for scholars
- How to use in Digital Humanities/DHist: What does quality for what purpose
How is text recognized?

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Automated Text Recognition?

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- Machine learning using neural networks
- Processes writing by line, rather than by character
- Needs to be trained by being shown document images and transcripts
- More training data --> more accurate recognition
- Create a model to transcribe and search a collection of documents
Automated Text Recognition

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- Neural networks can also process printed text – with less training data!
- Transcribe documents or use OCR engine in Transkribus
- Use these transcripts to train a model
- Results with 1-2% CER are possible
Recognising printed text

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- Making archival (esp. handwritten) documents more accessible
- New research infrastructure – Transkribus
- Funded until mid-2019 by the European Union
- 14 European partners
What is READ
Recognition and Enrichment of Archival Data

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- University of Innsbruck (co-ordinator / Austria) --> Transkribus
- Universitat Politecnica de Valencia (Spain) --> HTR
- University College London (United Kingdom) --> Dissemination, e-Learning
- National Center for Scientific Research “Demokritos” (Greece) --> Layout Analysis
- Democritus University of Thrace (Greece) --> Layout Analysis
- University of London Computer Centre (United Kingdom) --> Webinterface
- Vienna University of Technology (Austria) --> Layout Analysis, Writer Identification, STent
- University of Rostock (Germany) --> HTR, Layout Analysis
- Leipzig University (Germany) --> Dictionaries, Named Entities
- Xerox Research Centre Europe (France) --> Document Understanding
- Ecole Polytechnique Federale de Lausanne (Switzerland) --> Large Scale Demonstrator
- National Archives Finland (Finland) --> Large Scale Demonstrator
- Passau Diocesan Archives (Germany) --> Large Scale Demonstrator
READ Partner

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Projects with a
Memorandum of
Understanding

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15. Bentham model
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Based on Jeremy Bentham’s papers
(c.18-19 English)
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Written by Bentham and his
secretaries
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Trained on 896 pages – using
transcripts submitted by volunteers
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5-10% CER is possible

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1 Writer, 150 pages of trainingsmaterial:
10%
1 Writer, 450 pages of material for training:
4,4%
Same writer, 10 years later,
without material for training:
9,2%
1 Writer, 1132 pages of material for training:
3%
Text Recognition: What to expect
(Character Error Rate)

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X Writers, 800 pages of material
for training:
18%
X Writers, 100'000+ pages of
material for training:
?? %
Text Recognition: What to expect
(Character Error Rate)

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Let's get really started...
Transkribus: transkribus.eu / email@transkribus.eu / transkribus.eu/wiki/
READ: read.transkribus.eu (News)
Staatsarchiv Zürich: tobias.hodel@ji.zh.ch

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(Web-)Interfaces
- Transcription
- Crowdsourcing
- Correction
- Search/extract
- E-Learning (available)
- ScanApp (available)

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By UPVLC & HIMANIS (IRHT)
http://prhlt-kws.prhlt.upv.es/himanis/
(search for example for: «scriptor», play-around with „confidence“)
For further infos:
https://himanis.hypotheses.org/105
Keyword Spotting

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Transkribus export to be presented in EVT (by HumaReC)
http://humarec-viewer.vital-it.ch
Transkribus and EVT
Edition Visualization Technology

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●
10 Steps Guide
●
10 Steps Video
●
Transkribus Wiki
First Transkribus user day will take place in
Vienna, November 2nd
and 3rd
2017!
Find out more: