1. Oriented Local Binary Patterns for Offline Writer Identification
Handwriting as oriented texture
Anguelos Nicolaou, Marcus Liwicki, Rolf Ingolf
DIVA Group Fribourg Switzerland

2. Offline writer identification
sample
Image to feature vector
Vector should “uniquely” identify the handwriting style
Measure similarity between handwriting styles
Identify writer as the most similar
LBP
LBP
LBP
LBP DB
Features
LBP
LBP
LBP
LBP
LBP
All image samples in this presentation are taken from Icfhr 2012 competition on writer identification challenge

3. Local Binary Patterns Introduced by Ojala et al.
Each pixel has a circular neighborhood
Encode the binary relationship of each pixel in the neighborhood borders
Each pixel’s neighborhood is encoded as an integer
Bag of words approach
Total disregard towards structure between individual patterns

4. LBP parameters Radius: Sample count: Binary operator: Texture
Information
Structure
Smaller
Larger
Radius
Frequent events
Low detail
High detail
Scarce events
Less
samples
More
Samples
Sample
count

5. Proposed feature set: LBP tuning
8 samples in the periphery
3 pixel radius
Binary function: center equals periphery
Histogram of all occurring patterns except of “all white or all black” pattern normalized to a sum of 1

6. Proposed feature-set: High-order features
Derived from the LBP histogram
Histogram of rotation invariant hashes
Histogram of rotation phases
Histogram of border orientations
Histogram of border aggregations
Histogram of true bit count
Histogram of 0-1 transitions (beta function)
LBP
Histogram
High-order
Features
255 72
327

7. Transforming the feature space: Pipeline Overview
Feature extraction
Only Training
Raw Features
Train set
Raw Features
PCA
Component
Matrix
Rebase to components
Rebased Features
Train set
Labeled Features
Evolutionary
Algorithm
Scale
Matrix
Scale to optimal
Scaled Features
NN Classification

8. Transforming the feature space: PCA
Changing the basis of the feature space
Preserving 99.9% of the information
Reducing 327 to 125 dimensions
Should be perceived as training
With very good generalizing properties

9. Transforming the feature space: Scale vector optimization
Scale each dimension
Evolutionary optimization
Fitness:
minimize
Fitness inspired by LDA
2000 Generations
50 individuals per population

10. Performance measurement
Train-set: ICFHR 2012 (100 writers x 4 samples) “Icfhr 2012 com- petition on writer identification challenge”
Test-set: ICDAR 2011 (26 writers x 8 samples) “Icdar 2011 writer identification contest”
NN classification:
Method
Soft 10
Hard 7
NN accuracy
Tsinghua
(2011 winner)
100%
44.1%
99.5%
Untrained
99.04%
28.84%
96.63%
Trained
50.48%
98.56%

11. Qualitative experiments: Rotation robustness
Rotation correction destroys slant information
Tolerance +- 5 degrees
Conclusion: Quite robust to rotation
-20o
-5o 0o

12. Qualitative experiments: Robustness against scale
Typically, methods are sensitive to scale
NN classification
Dataset: ICHFR 2012
Intolerant of comparing samples at different scales from the db
Discriminative ability preserved when rescaling samples and db

13. Qualitative experiments: Signal quantity requirements
NN classification
Dataset: 2011 ICDAR
Gradually removed connected components from top to bottom
For the specific dataset, the method requires 3-4 text-lines.
25%
100%

14. Qualitative Experiments: Writer vs Writing Style
SigWiComp2013 train set 55 writers 3 samples
Performance of winner 28% SigWiComp2013. Our system 19.4%
Split in left and right halves
Task: assign left half to right half
Assigning correctly two pieces of the same sample: 86.06%
Assigning correctly two pieces of the same writer: 87.27%
Conclusion:
Our method practically treats different writing styles as different writers

15. Conclusions: Powerful feature set
Good behavior in trained and untrained modalities
Perceives handwriting as texture
Reasonably robust against geometric distortions
As is, probably unfit for biometric applications

16. Use cases of LBP feature set
Thank you for your attention