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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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Presentation Transcript
slide1
Ralph Niels & Louis Vuurpijl

Nijmegen Institute for Cognition and Information

Radboud University Nijmegen

The Netherlands

Generating copybooks from consistent handwriting styles

overview
Overview
  • Handwriting styles and copybooks
  • Method
  • Results
  • Discussion
handwriting styles
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.

applications
Applications
  • Handwriting recognition
    • Personalized recognizers
  • Handwriting synthesis
    • ‘Handwriting fonts’
  • Forensic writer identification
    • Human experts use the notion of style

K. Franke, 2005

method 1
Method (1)

Data

Data

slide6
Data
  • Databases:
    • Unipentrainset
    • Unipendevset
    • Plucoll database
  • Online handwritten characters(pre-segmented)

43 writers

41 writers

clustering of prototypes
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.

clustering of prototypes1
Clustering of prototypes

PCi

Prototype clusters

PCj

PCk

Prototypes

create membership vectors
Create membership vectors
  • Relative frequency of the occurrence of each prototype cluster in a persons handwriting
create membership vectors example handwriting x
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

create membership vectors example handwriting x y and z
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

find writing styles
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

select consistent handwriting styles
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

select consistent handwriting styles1
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
results
Results
  • Copybooks
  • Preliminary results
  • Visual evaluation by handwriting experts
  • Meaningful names
  • Well-known broad categories: cursive, mixed and print
discussion
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