Optical Character Recognition (OCR) technology can help users in their research by digitizing printed texts and enabling full-text search. However, OCR quality varies and error rates can be as high as 10-40% depending on factors like language and publication date. This can negatively impact researchers seeking all occurrences of search terms. Crowd-sourcing corrections for searched words and utilizing external knowledge sources like Wikipedia could help improve search results and researchers' experiences. Machine learning applied to large digitized collections also has potential to extract additional useful information and insights not readily apparent from the text alone.

1. How can Optical Character
Recognition technology help users in
their research?
Günter Mühlberger, Innsbruck University
Digitisation and Digital Preservation group

2. Agenda
Part 1: Optical Character Recognition – Some basics
Part 2: Users – The Unknown Creature?
Part 3: Some ideas!

3. Part 1
Some basics on
Optical Character Recognition (OCR)
(a story about errors…)
3

4. Berufsgenossenschaften
IMPACT
EVA/MIN
ERVA
12th Nov.
2008
4

5. Digitisation and OCR
• Digitisation of historical printed material
• Google: Billions of files, libraries: Millions of files
• Google books: Would never have started without full-text
• BNF: Partner in EU Project METADATA ENGINE (2000-2003, ABBYY Historical OCR)
• OCR quality
• There are only a few reliable data on the accuracy of OCR on large scale datasets
• E.g. we do not know „how good the Google collection“ is as a whole, or per language, per
century, decade or year, per text type, etc.
• Simon Tanner (2009)
• Has done evaluation of OCR accuracy on British Newspapers
• Differences per newspaper are stronger than per publishing date
• Overall we are speaking about 10% to 40% Word Error Rate, with an average of 22% WER
for standard words and 31% for significant words
• Evaluation done within the IMPACT project has shown similar figures

6. IMPACT
EVA/MIN
ERVA
12th Nov.
2008
6
und wenn
???

7. 83,4 % Correct Words for French in EU News
7

8. Part 2
Users
(the unknown creature)
8

9. (1)
Occasional users
9
Typology of users

10. Occasional users
10

11. Occasional users – Google Analytics
11

12. Occasional users
•Occasional users
• Come by coincidence or curiosity
• Just typing in something without real interest in the results
• Developers of websites
• Test users for new websites
• Decision makers for digital library projects
• More interested in features than in content
12

13. (2)
Researchers
13
Typology of users

14. Researchers  Scholars
14

15. Researchers
•Definition
• Anyone who is actually looking for some specific content and
invests some reasonable time into these investigations
• Professional researchers (e.g. historians,…)
• Students (e.g. writing their thesis)
• Family historians (e.g. searching for their family members)
•Citizen scientists (e.g. writing Wikipedia articles)
• Volunteers (e.g. contributing to improve OCR text)
• Teachers (e.g. preparing lessons)
• School pupils (e.g. doing their homework)
• Etc.
15

16. Researchers
• Researchers are not searching a collection because they WANT
to search the full-text – it is just a tool to satisfy their need for
information!
• Researchers are looking for answers on their specific questions!
• Was my grandfather mentioned in the local newspaper when he returned
from first World War?
• What was written about my village in 1870?
• Are there interesting news from the French Revolution in a newspaper
from Vienna in 1789?
• How were companies advertising their products in 1750, 1850 and 1950
in newspapers?
• How did newspapers write about “sex and crime” in 1900?
• How did people find new jobs in the early 19th century?
16

17. What researchers are doing with their sources
•Read articles
• Researchers want to know what is written in an article
•Download – Collect – Print out
• Researchers are conservative and pragmatic in organising their
work
• Want to work on their own computers, want to read offline, etc.
•Work
• Collecting the material is just the beginning
17

18. Annotate
18

19. Excerpt
19

20. Arrange
20

21. Fill databases
21

22. Analyse
22

23. Draw conclusions
23

24. Exchange with others
24

25. Cite text
25

26. Link sources
26

27. Write publications
27

28. And many, many other activities…
28

29. (3)
Machines
29
Typology of users

30. Machines as users
30

31. Machines as users
• Google
• Is just the beginning (though an important one)
• Facebook, LinkedIn, Academia.edu,…
• Image you could see from all users in Gallica their affiliation to a social
network!
• You would get the “social graph” of these users and therefore also see
(understand) all connected users
• Machines like very much
• Rich data (machine generated)
• Standardized formats (XML)
• Normalized data
• Clear distinction of metadata and content data
• Permanent links
• Open Data
• …
31

32. Part 3
Some ideas…
32

33. (1)
Back to the sources!
33
Ideas

34. Source critics
•Get to know your source!
• Attitude of historians: Don’t trust your source!
•Researchers need to know “What is my source? How
reliable is it? What can I find, what not?”
•Needs to be applied to OCR as well!
• Simple information:
• Number of pages per average day, month, year, decade, century
• Number of words/articles on a page
• Number of words missed on a page due to OCR errors
• Etc…
34

35. Tools
•Users need to know more about the quantitative shape of the
collection they are searching
• The number of pages is increasing during the centuries
• The number of words on a page is increasing until the 1950ies
• The number of photos is increasing from the 1920ies onwards
• The number of OCR errors (missing hits when searching) is in
general decreasing but depends on many other factors as well
35

36. Mapping Texts – Univ. North Texas and Stanford
36

37. (2)
Natural involvement – search and correct!
37
Ideas

38. 10-30% errors…
•What does this mean for the researcher?
• For reading a page they have the original image
• Simply because the OCR has errors they will miss e.g. 20% of all
occurrences of a search term!
•Maybe acceptable to specific use cases, but surely not for
humanities scholars or family historians: They want to get
„all relevant occurrences“
• What is “relevant” is decided by the user, some may be interested
just within a specific time period, or periodical, or collection of
documents
• Note: Not all words are frequent in all collections („London“ in a
Tyrolian newspaper collections is seldom whereas it is frequent in
a British Newspaper Collection)
38

39. Australian National Library
39

40. Australian National Library
40

41. Searching AND correcting
• Let‘s combine searching and crowd based correction!
• Provide users with a powerful instrument to correct exactly
those words where they are interested in (searching for)
•Relieve users from actually editing words, but let them just
approve or reject the results of the OCR engine
41

42. Interface with Word Snippets
42

43. OCR errors
43
neue nelle neue nelle

44. Select correct word images = green = approved
44

45. Consequences
•User corrects exactly those words he is looking for
• Together with an annotation tool he will be able to find ALL
OCCURENCES of a search term and e.g. tag them as
important, less important, etc.
•Other users will benefit (and see) the corrections carried out
by another user
• Export feature where all occurrences are put together in one
PDF would be a next step…
45

46. (3)
Knowledge based searching
46
Ideas

47. What users get now with full-text searching
47

48. What they would like to get: Overview
AND detail
48

49. Named Entities and Wikipedia Linking
49
Search for “Vranitzky”
Number of hits in full-text
and on article level
List of Persons, Institutions and geographical
Names appearing in the articles with “Vranitzky”

50. Named Entities integrated into search interface
50
Search for “Vranitzky” AND
“Wolfgang Schüssel”

51. Article about Schüssel AND Vranitzky
51

52. Wikipedia Categories
52
Search for “Vranitzky” retrieves
also
(1)The fact that it is the person
“Franz Vranitzky”
(2)the categories in Wikipedia of
this person

53. Utilizing Wikipedia Knowledge
53
Search for
“Bundeskanzler_Österreich”
(chancellor Austria) retrieves
(1)All other chancellors from
Austria appearing in the
newspaper
(2)All articles connected with
this category

54. The new Encyclopédie  Gallica
54

55. (4)
Let’ machines play – and learn!
55
Ideas

56. Let ‘em play!
• Progress/Innovation
• = Computer Scientists + User needs + Data (from libraries)
• Computer Science
• Break through in face and speech recognition, big data analysis, recommender systems,
information retrieval is based on statistical methods!
• Statistical algorithm need data
• Metadata are not enough (though important)!
• Sample data are not enough!
• The more data the better
• An example
• If you have 10 mill. digitized newspaper pages published within 200 years. How many pages
do you have on average per day?
• 136!
• We have done 2 mill. pages for BNF within EU Newspapers!
• The easier to access the data, the better!
• Download (simple, easy, fast, cheap!)
• Nice to have: APIs and dedicated web-services (something for real experts)
56

57. What machines (computer scientists) can do…
• Information extraction
• Get names of persons, locations,
• Images within printed text (photos…)
• Book titles (reviewed), theatre plays, advertisments,…
• But also: facts about car accidents, sex and crime, stock exchange rates,
• And: Sentiment analysis…
• Linking of text with external sources
• A lot of the information in (historical) newspapers can be found elsewhere
in a much better way
• Start of World War I
• Dreyfuss – Affair
• German “Reichstagswahl” in March 1933
• Wikipedia was just a simple example…
57

58. Machine learning
58

59. Thank you for your attention!
Contact: Günter Mühlberger
<guenter.muehlberger@uibk.ac.at>
www.europeana-newspapers.eu