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Generating copybooks from consistent handwriting styles

Ralph Niels & Louis Vuurpijl Nijmegen Institute for Cognition and Information Radboud University Nijmegen The Netherlands. Generating copybooks from consistent handwriting styles. Overview. Handwriting styles and copybooks Method Results Discussion. Handwriting styles.

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Generating copybooks from consistent handwriting styles

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  1. Ralph Niels & Louis Vuurpijl Nijmegen Institute for Cognition and Information Radboud University Nijmegen The Netherlands Generating copybooks from consistent handwriting styles

  2. Overview • Handwriting styles and copybooks • Method • Results • Discussion

  3. Handwriting styles • Handwriting is individual • Similar handwritings: handwriting styles • Top down ‘copybooks’ * • We defined writing styles bottom up * S.-H. Cha, S. Yoon, C.C. Tappert, 2006.

  4. Applications • Handwriting recognition • Personalized recognizers • Handwriting synthesis • ‘Handwriting fonts’ • Forensic writer identification • Human experts use the notion of style K. Franke, 2005

  5. Method (1) Data Data

  6. Data • Databases: • Unipentrainset • Unipendevset • Plucoll database • Online handwritten characters(pre-segmented) 43 writers 41 writers

  7. Method (2) Data

  8. Clustering of prototypes • The prototype we used areaveragedshapes of actualhandwrittencharacters L. Vuurpijl & L. Schomaker, Finding Structure in Diversity, ICDAR 1997. R. Niels, L. Vuurpijl & L. Schomaker, Automatic allograph matching inforensic writer identification, IJPRAI, Feb. 2007.

  9. Clustering of prototypes PCi Prototype clusters PCj PCk Prototypes

  10. Method (3) Data

  11. Create membership vectors • Relative frequency of the occurrence of each prototype cluster in a persons handwriting

  12. Create membership vectors(example: handwriting X) 1.0 PCi Prototype clusters PCj PCk 0.2 0.8 Prototypes 0.15 0.05 0.05 0.52 0.23

  13. Create membership vectors(example: handwriting X, Y and Z) Handwriting Y 0.47 0.41 0.00 0.09 0.03 Handwriting X 0.15 0.05 0.05 0.52 0.23 Handwriting Z 0.12 0.01 0.22 0.55 0.10

  14. Method (4) Data

  15. Find writing styles • Hierarchical clustering of membership vectors (handwritings) Writing styles B H Z X E D I A G B J K Y C F Handwriting

  16. Method (5) Data

  17. Select consistent handwriting styles Cluster parameters PCi Level selection PCj PCk B H Z X E D I A G B J K Y C F Cluster parameters Level selection

  18. Select consistent handwriting styles • Monte Carlo simulation of combinations of parameters and levels • Large number of writing styles • Find the writing styles that occur most • By prototypes or • By writers

  19. Results • Copybooks • Preliminary results • Visual evaluation by handwriting experts • Meaningful names • Well-known broad categories: cursive, mixed and print

  20. Results (example handwritings) Print Cursive Mixed

  21. Results (prototype occurrence) Print Cursive Mixed

  22. Discussion • Applied to/with, not limited to: • Online Latin characters • Dynamic Time Warping for character comparison (human congruous) • Best of both worlds: Integrate top down and bottom up (with forensic experts) B H Z X E D I A G B J K Y C F Integrate

  23. Questions?

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