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Symbol Recognition Contest 2009current status
Philippe Dosch1, Ernest Valveny2 and Mathieu Delalandre2
1LORIA, QGAR team, Nancy, France2CVC, DAG Group, Barcelona, Spain
GREC 2009 WorkshopLa Rochelle, France
Thursday 23th of July 2009
Introduction
• Context
Many recognition methods exist, sometimes very ad-hoc and domain dependent
Which are the most generic/robust ones? Able to recognize a large variety of data, from different application domains Robust to common noise and distortion found in documents Easy to implement and/or tune
Objective: Measure their performance and robustness under different criteria and kinds of noise
Introduction
Past recognition contests:
ICPR’00, GREC’2003, GREC’2005 and GREC’2007
Contest evolution
ICPR’00, GREC’2003, GREC’2005 segmented technical symbols
GREC2007 segmented logos
GREC2009 whole drawings (i.e. symbol localization)
Agenda
by 31th of July training datasets will be available
http://dag.cvc.uab.es/isrc2009/
by OctoberThe contest will be run online http://epeires.loria.fr/
Interested people are invited to participate
Introduction
• Concerned data
Plan
Recognition datasets (segmented technical symbols and logos)
Localization datasets (drawings, queries)
Conclusions
Recognition Datasets
• 10-25 images/class• All classes included
Basic dataset
Scalability …
Subsets of the basic dataset with increasing number of classes (25, 50, 100, 150)
Geometrictransformations
…
Application of rotation and scaling to the images of the basic dataset
Image degradations
…
Application of increasing levels of degradation to the images of the basic dataset (for each kindof degradation)
…
Recognition Datasetsnoise A noise B noise E
Recognition Datasets
Domain Nº of symbol models
Nº of images / symbol models
Symbols Noise
Technical 150 10 1500 Rotation
Technical 150 10 1500 Scaling
Technical 150 10 1500 Rotation and Scaling
Technical 150 25 3750 Noise A (1-5)
Technical 150 25 3750 Noise B (1-5)
Technical 150 25 3750 Noise E (1-6)
15 750
Logos 105 10 1050 Rotation
Logos 105 10 1050 Scaling
Logos 105 10 1050 Rotation and Scaling
Logos 105 25 2625 Noise A (1-5)
Logos 105 25 2625 Noise B (1-5)
Logos 105 25 2625 Noise E (1-6)
14 775
Training sets
Localization Datasets
c2
c1
M1
M2
M3
M4
C1
C2
C3
C4
L1
θ1
p1
L2θ2
p2
p
1,0L 2,0
L
bounding box and control point
alignment
symbol model loaded symbol
Symbol Models
BuildingEngine
(2) run
(3) display
(1) edit
Background Image
Localization Datasets
Localization Datasets
Groundtruth
Generation of queries
1. Random selection of a document2. Radom selection of a symbol
3. Random crop
Background Dataset 1
Random selectionof a test image
with groundtruth
Background Dataset 2
Background Dataset n
---
Image degradation
Contest Dataset 1
Contest Dataset 2
Contest Dataset n
---
Localization Datasets
Level 1 Level 2 Level 3
Localization Datasets
Type Domain Nº of symbol models
Images Symbols Noise
Drawings Architectural 16 20 633 ideal
Drawings Architectural 16 20 597 level 1
Drawings Architectural 16 20 561 level 2
Drawings Architectural 16 20 593 level 3
80 2384
Drawings Electrical 20 20 246 ideal
Drawings Electrical 20 20 274 level 1
Drawings Electrical 20 20 237 level 2
Drawings Electrical 20 20 322 level 3
80 1079
Queries Both 36 900 900 NA
900 900
Conclusions
New feature of the contest, localization datasets
Remaining work, performance characterization for localization
simple method (e.g. bounding box overlapping)
Agenda
by 31th of July training datasets will be available
http://dag.cvc.uab.es/isrc2009/
by OctoberThe contest will be run online http://epeires.loria.fr/
Interested people are invited to participate,
please contact us: [email protected]
