Jesse de Does gives presentation on the LMU OCR Profiler and Post Correction. Delivered at British Library Demo Day on the 12th of July 2011.

1. TR5 Profiler and Post-Correction System Ludwig-Maximilians-Universität München Centrum für Informations- und Sprachverarbeitung

10. Korrektursystem

11. Korrektursystem

16. Historical variant and OCR error patterns Historical Variants OCR Error patterns teil  theil theil  iheil

17. Relative frequency: 2.9% of all ‘t’ are rewritten to ‘th’ Absolute frequency: Pattern was found 120 times in the current document.

25. Evaluation: Measures (1) Global Profiles Percentage of matches for the first 10 patterns in the ranked output lists Two Values: Historical Patterns, OCR Patterns (2) OCR Error Detection Precision and Recall for the OCR errors detected by the Profiler (3) Indirect evaluation (For instance, by means of the postcorrection system)

26. Evaluation: Data preparation (1) Deep Evaluation: For each token of the evaluation document the historical interpretation and the OCR interpretation have been manually annotated. ++ fully accurate -- manual work (2) Shallow Evaluation: The OCR’ed document is automatically aligned with its re-typed ground truth; For each token of the evaluation document the historical and the OCR interpretation is automatically assigned from the ground truth. ++ no manual work – not completely accurate

27. Evaluation: Data Deep: Eckartshausen 100 pages Briefkunst 40 pages Shallow: 5 books each, 16 th , 17 th and 18 th century

29. Graphical Evaluation: Eckartshausen

30. Graphical Evaluation: diacritics Hist. Var. OCR

31. Shallow Evaluation Results 16th 17th 18th HIST Patterns first 10 60% 74% 78% OCR Patterns first 10 48% 70% 50% Error Detection Prec 95% 92% 81% Error Detection Recall 49% 43% 45% Content Words Errors 64% 44% 16% Easy Interactive Correction per 10,000 words ≈ 3000 words ≈ 1892 words ≈ 720 words