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Oriented Local Binary Patterns for Offline Writer

IdentificationHandwriting as oriented texture

Anguelos Nicolaou, Marcus Liwicki, Rolf IngolfDIVA Group Fribourg Switzerland

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Offline writer identification

Image to feature vector

Vector should “uniquely” identify the handwriting style

Measure similarity between handwriting styles

Identify writer as the most similar

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LBP

LBP

LBP

LBP

LBP

LBP

LBP

LBP

LBP

DB

All image samples in this presentation are taken from Icfhr 2012 competition on writer identification challenge

Features

sample

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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

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LBP parameters

Radius:

Sample count:

Binary operator:

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TextureInformation

StructureInformation

Smaller LargerRadius

Frequent eventsLow detail

High detailScarce events

Lesssamples

MoreSamples

Sample count

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eg binary : center ≠ edge, center = edge,center∧edge

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eg grayscale : center > edge, center < edge,center ≥edge

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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

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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)

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255 72

High-orderFeatures

327

Histogram

LBP

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Transforming the feature space:Pipeline Overview

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Train setRaw Features

Train setLabeled Features

Component Matrix

Scale Matrix

Feature extraction

Rebase to components

Scale to optimal

PCA

EvolutionaryAlgorithm

Raw Features

Rebased Features

Scaled Features

NN Classification

Only Training

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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

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Transforming the feature space:Scale vector optimization

Scale each dimension Evolutionary optimization Fitness:

minimize

Fitness inspired by LDA 2000 Generations 50 individuals per population

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distance of same class samples∑distance of different class samples∑

⎛

⎝ ⎜ ⎜

⎞

⎠ ⎟ ⎟

each sample

∑

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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:

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Method Soft 10 Hard 7NN

accuracy

Tsinghua (2011 winner)

100% 44.1% 99.5%

Untrained 99.04% 28.84% 96.63%

Trained 99.04% 50.48% 98.56%

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Qualitative experiments:Rotation robustness

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• Rotation correction destroys slant information

• Tolerance +- 5 degrees• Conclusion: Quite robust

to rotation

-20o

-5o

0o

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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

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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.

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25% 100%

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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

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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

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Use cases of LBP feature set

Thank you for your attention

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