1. Europeana Newspapers -
Refinement Workshop
WP2 – Introduction to Refinement
Belgrade, 13 June 2013
Clemens Neudecker (@cneudecker)

2. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Overview
• Objectives & Challenges
• Overview of Refinement Dataset
• Introduction to Refinement: Workflow & Technologies
• Questions & Answers
2

3. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Objectives
- Analysis of available digital newspaper collections of project partners
and identification of subsets suitable for refinement
- Definition of requirements and minimum quality of digitized newspapers
for refinement to enable advanced services in Europeana
- Coordination of the scalable processing of 10 million digitised newspaper
pages with several refinement technologies
- Providing recommendations on best practices for the refinement of
digitised newspaper collections with full-text (and ingest to Europeana)

4. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Challenges
• Processing quality vs. speed/throughput
• Volume of data requires focus on simple &
standardised workflow with clear checkpoints
• Diverse partners supplying content with different
digitisation & access policies
• Large variety of content in terms of file formats,
fonts, languages, etc.
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5. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
The data

6. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Europeana Newspaper Dataset (1)

7. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Europeana Newspaper Dataset (2)

8. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Europeana Newspapers Dataset (3)

9. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Europeana Newspapers Dataset (4)

10. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Refinement Workflow steps
10

11. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Master List

12. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Tools (BCT)
• BCT = Binarisation and Colour Reduction Tool
• Purpose: Convert grey/colour
scans to bitonal using highly
optimised GPP method
• Background: Reduce total file
size of master images to
guarantee feasibility and
timing of data transfers
12

13. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Tools (FRT)
• FRT = File Rename Tool
• Purpose: Support content
holders in preparing their
data in the correct format
• Background: Ensure folder
structure and file naming
requirements for automated
processing are met
13

14. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Tools (FAT)
• FAT = File Analyzer Tool
• Purpose: Final quality check
of data before refinement
• Background: Ensure content
and refinement partners that
all preparation steps have
been executed successfully
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15. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Refinement: OCR@UIBK
• OCR = Optical Character Recognition
• Number of pages to be refined: 8 million
• Technologies: ABBYY FineReader SDK
• State-of-the-art OCR software, fully supports Fraktur/Latin/Cyrillic fonts
• Result: METS/ALTO package containing images, metadata & full text
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16. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Refinement: OLR@CCS
• OLR = Optical Layout Recognition
• Number of pages to be refined: 2 million
• Technologies: docWorks
• Separation of columns, articles, headlines, page classes
• Result: METS/ALTO package containing images, metadata & full text
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17. This project is partially funded under the ICT Policy Support Programme (ICT PSP) as part of the
Competitiveness and Innovation Framework Programme by the European Community
http://ec.europa.eu/ict_psp
Refinement: NER@KB
• NER = Named Entities Recognition
• Number of pages to be refined: 2 million
• Technologies: Stanford CRF-NER
• Languages supported: German, Dutch, English (+ French, Latvian)
• Open source: https://github.com/KBNLresearch/europeananp-ner
• Detection of Named entities: Person, Location, Organization
• Feedback cycle with manual training step  better results
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18. Thank you for your attention -
Now come ask me almost
anything!
clemens.neudecker@kb.nl