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Wrapper Induction for List Extraction from OCRed Documents

This study explores Wrapper Induction methods for extracting lists from OCR data, improving data entry efficiency and accuracy. Techniques include Weakly-supervised and Semi-supervised Wrapper Induction using Hand-Labeled OCR data. Preliminary results show promising F-measure results on small datasets. Future work focuses on enhancing rules for list extraction.

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Wrapper Induction for List Extraction from OCRed Documents

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  1. ListReader:Wrapper Induction for Lists in OCRed Documents Thomas Packer BYU CS DEG 2012.03.17

  2. We Love Data (In Digital Form)

  3. Lots of Paper Documents in the World

  4. Lots of Text in Paper Documents

  5. Lots of Lists in TextLots of Data in Lists

  6. Manual Data Entry

  7. Wrappers: Individualized Extraction Rules

  8. Semi-Automatic Wrapper Induction

  9. Weakly-supervisedWrapper Induction Semi-supervised Wrapper Induction

  10. Data: Image

  11. Data: OCR

  12. Data: Hand-Labeled OCR

  13. Induced Regex Wrappers Single-Specific Character Classes (i)(\. )(Lydia)( )(Lewis)(\*, )(b\. ) … [a-z] [A-Z] [0-9] [ \t\r\n] <each punct.> Single-General ([a-z]{1,5})([ \t\r\n\.]{1,6})([a-zA-Z]{3,9})([ \t\r\n]{1,5}) ([a-zA-Z]{3,9})([ \t\r\n\*,]{1,7})([ \t\r\n\.a-z]{1,7}) … Multi-General ([iv]{1,3})([ \.]{2,2})([ACHJLacdehilmnrstuvy]{4,6})([ ]{1,1}) ([Leisw]{5,6})([ \*,]{2,3})([ \.abp]{3,5}) …

  14. Preliminary Results(Field Label F-measure, Small Dataset)

  15. Conclusions and Future Work

  16. All done. Suggestions?

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