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Introducing TRIGRAPH trimodal writer identification

Introducing TRIGRAPH trimodal writer identification. Ralph Niels * , Louis Vuurpijl * and Lambert Schomaker ♦. * Nijmegen Institute for Cognition and Information Radboud University Nijmegen. Dutch Forensic Institute. ♦ Artificial Intelligence Institute University of Groningen.

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Introducing TRIGRAPH trimodal writer identification

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  1. Introducing TRIGRAPHtrimodal writer identification Ralph Niels*, Louis Vuurpijl*and Lambert Schomaker♦ *Nijmegen Institute for Cognition and Information Radboud University Nijmegen Dutch Forensic Institute ♦Artificial Intelligence Institute University of Groningen ENFHEX conference - November 2005 – Budapest, Hungary

  2. Overview • Computer assisted document examination • TRIGRAPH combines 3 methods:I Automatic features from imageII Manually measured propertiesIII Allographic features • Recent achievement: “intuitive” matching • Summary • Next steps

  3. Computer assisted document examination

  4. Computer assisted document examination

  5. Improving on current systems • Systems do not benefit from recent advances in pattern recognition and image processing • New insights in: • automatically derivedhandwriting features • user interface development • innovations in forensic writer identification systems • Aim: Suspected document in top-100 hit list from database of > 20,000 writers

  6. Design requirements • Improve on currently available performance • Minimize amount of manual labor • Exploit human cognition and expertise • Correspond to expectations of human experts

  7. WANDA • Integrate techniques in WANDA Workbench(Franke et al., ENFHEX News 2004; Van Erp et al., JFDE (16) 2004)

  8. Three approaches I Automatic features from images II Manually measured properties III Allographic features

  9. I Automatic features from images (1) • Layout and spacing • Ink morphology (Franke)

  10. I Automatic features from images (2) • Local shape (Bulacu)

  11. I Automatic features from images (3) • Grapheme-fraglet tables (Schomaker)

  12. Manually measured properties II • Fish • Script • Wanda

  13. III Allographic properties (1) • (Vuurpijl, Niels) Matching characters by: • Considering global shape characteristics • Reconstructing and comparing production process • Zooming in on particular features

  14. 10 7 1 1 III “Intuitive” matching (1) • Given: 2 dynamic trajectories(one questioned, one from aset of prototypes) • Technique: Dynamic TimeWarping (point-to-pointcomparison) • Result: similarity measure thatcan be used to find prototypethat is most similar toquestioned sample

  15. III “Intuitive” matching (2) • Experiment: compare various techniques • Result: Dynamic Time Warping yields visually convincing (or “intuitive”) results • Our work on DTW was previously presented at: • 9th International Workshop on Frontiers in Handwriting Recognition(IWFHR-2004), Japan. • 12th Conference of the International Graphonomics Society(IGS-2005), Italy. • 8th International Conference on Document Analysis and Recognition(ICDAR-2005), South-Korea.

  16. III Allographic properties (2) • (Semi-)automatic extraction of dynamic information: • Automatically extract traces from scanned document • Verify resulting trajectories with allograph prototypes • Start user-interaction in case of confusion • Advantages: • More reliable measurements • Online character recognition techniques • Search for particular allographs in documents • Visually convincing matching techniques

  17. Summary • Computers can help forensic experts in measuring handwriting and searching databases • In TRIGRAPH, new insights from different scientific areas will be used • In TRIGRAPH, new UI methods will be combined with techniques developed in three modalities:I Automatic features from imagesII Manually measured propertiesIII Allographic features

  18. Next steps • Automatic extraction of dynamical information from scanned images • Supervised character segmentation • Allograph based verification of results

  19. Introducing TRIGRAPHtrimodal writer identification Ralph Niels*, Louis Vuurpijl*and Lambert Schomaker♦ *Nijmegen Institute for Cognition and Information Radboud University Nijmegen Dutch Forensic Institute ♦Artificial Intelligence Institute University of Groningen Questions? ENFHEX conference - November 2005 – Budapest, Hungary

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