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BL Demo Day - July2011 - (7) OCR Profiler and Post-Correction

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.

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TR5 Profiler and Post-Correction System  Ludwig-Maximilians-Universität München Centrum für Informations- und Sprachverarbeitung
TR5 Post-Correction System ,[object Object],[object Object],[object Object]
Customizable user interface ,[object Object],[object Object],[object Object],[object Object],OCR and image fragments Correction candidates, Special functions Complete image Font size
[object Object],[object Object],View: OCR and Image clippings
[object Object],[object Object],[object Object],View: Original image
[object Object],[object Object],[object Object],Word by word correction of text

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BL Demo Day - July2011 - (7) OCR Profiler and Post-Correction

  • 1. TR5 Profiler and Post-Correction System Ludwig-Maximilians-Universität München Centrum für Informations- und Sprachverarbeitung
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  • 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.
  • 18.
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  • 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
  • 28.
  • 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

Editor's Notes

  1. DictModule name=“modern” File=“../dicts/modern.dic” max_ocr_errors=3 max_spelling_variants
  2. DictModule name=“modern” File=“../dicts/modern.dic” max_ocr_errors=3 max_spelling_variants