1. Senka Drobac and Pekka Kauppinen and Krister Lindén
Improving OCR of historical
newspapers and journals
published in Finland
1

2. Motivation
•Corpus of historical newspapers and magazines that has been
digitized by the National Library of Finland
•OCR was done with commercial software Abbyy FineReader
•Character accuracy rate (CAR): ~ 90-91%

3. Figure from: Vesanto, Aleksi, et al. "A system for identifying and exploring text repetition in
large historical document corpora." Proceedings of the 21st Nordic Conference on
Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden. No. 131.
Linköping University Electronic Press, 2017.

4. Ocropy
•Decided to train models with Ocropy
•Ocropy:
• Open source, uses LSTM, line based
• Tools for preprocessing, segmentation, training, recognition,
evaluation
• Above 98.5% CAR on German 19th and 20th century

5. OCR workflow (Ocropy)
Image
Line
segmentation Line
images
Text
(lines)
Text
(lines)
Binarized
Image
(Pre-processing)
Binarization OCR
Post
processing
trained model

6. Data
1771 - 1939
Languages: Finnish and
Swedish
Typefaces: Fraktur and
Antiqua

7. • Good quality
• Finnish Fraktur
• One column

8. • Good quality
• Swedish Antiqua
• Two columns

9. • Binarized image
• Difficult segmentation

10. • Binarized image
• Challenging segmentation
• Many different fonts on one
page

11. • Both Finnish and Swedish
on the same page

12. • Poor quality

13. Line examples - Fraktur
☛ För billigt pris: En kursläde i garden
Sananlennätinkonttori awoinna joka päiwä
-— Salama iski tiistai yönä klo
pitänyt tarpeellisena warata jonkunlaisen

14. Line examples – Antiqua
osakkaat kutsutaan täten varsinaiseen yhtiö-
nuksia määräämälleen rautatiease-
m stammanträda i nämnde kontors loka
Heines poetische Werke. I två band. 17 m.

15. Goal
•Train a model that would be able to recognize everything

16. Previous work
•Ocropy + post correction (FST)
•Finnish data sets:
• CAR: 93.5% - 94.83%
• After post-processing CAR: 93.68% - 95.21%
•It is better to randomly sample 10 000 lines from the entire
corpus than train on all lines from 250 pages

17. • Lots of Swedish material → add Swedish training data
Finnish:
~10 000 training lines
(randomly picked)
~75% Fraktur, ~25% Antiqua
Swedish:
~ 6 000 training lines
(randomly picked)
~50% Fraktur, ~50% Antiqua

18. Experiments
model fin-test
fin-3k 94.0 / 76
fin-4k 94.1 / 77
fin-5k 93.2 / 72
fin-6k 94.4 / 77
fin-7k 94.5 / 78
fin-8k 94.3 / 77
fin-9k 94.0 / 76
fin 93.9 / 75
Results show CAR (%) / WAR (%)

19. Experiments
Not enough Finnish
Antiqua in training
Results show CAR (%) / WAR (%)
Finnish results improve with
additional Swedish data

20. Future work
•Need more Finnish Antiqua data
•1D LSTM too small memory → use Deep Neural Networks
Calamari-ocr

21. Thank you!
senka.drobac@helsinki.fi