some shit

1. End-to-End Text Recognition with
Convolutional Neural Networks
Tao Wang*, David J. Wu*, Adam Coates, Andrew Y. Ng
Computer Science Department
Stanford University
* Denotes equal contribution

2. Tao Wang 2
Scene Text Recognition Overview
• Text “in the wild” are hard to recognize
• Wide range of variations in backgrounds,
textures, fonts, and lighting conditions
Street View Text Dataset
K.Wang et al., 2011
ICDAR 2003 Dataset
S. Lucas et al., 2003

3. Tao Wang 3
Detection/Classification High-level Inference
“HOTEL”
Two-Stage Framework

4. Tao Wang 4
Exhaustive
Graph Search
MSER + SVM with RBF
Kernel
Neumann and
Matas, 2012
CRF + N-gram
model
HOG + SVM with RBF
Kernel
Mishra et al., 2012
Pictorial
Structure
HOG + Random Ferns
K. Wang et al., 2011
Semi-Markov
CRF
Appearance + Geometry
Weinman et al.,
2008
High-level
inference
Classification and
detection
Works

5. Tao Wang 5
Simple
off-the-shelf
heuristics
Learnt features +
2-layer CNN
Our approach
Graph based
inference
models
Hand-designed
features + off-the-shelf
classifier
Most other
approaches
High-level
inference
Classification and
detection

6. Tao Wang 6
ICDAR 62-way cropped
character classification
Detection/Classification End-to-end system after high-level inference
Various Benchmarks
ICDAR and SVT end-to-end text recognition
ICDAR and SVT Cropped
word recognition Lexicon
SOTA
SOTA on ICDAR SOTA

7. Tao Wang 7
Unsupervised Feature Learning
Contrast Normalization + ZCA whitening
K-Means
Coates et al., 2011

8. Tao Wang 8
Convolution Convolution
Spatial Pooling Spatial Pooling
L2-SVM Classifier
√ Text × Non-Text
Backpropagation
Large representation but not enough data.
Overfitting?
96
256
~10K parameters for detection
~50K parameters for classification
1st layer 2nd layer

9. Tao Wang 9
Synthetic Data
Color Statistics
Synthetic “hard negatives”
Real Synthetic
Unrealistic Synthetic Data
Real Data
Java.Font + Natural backgrounds

10. Tao Wang 10
Detector Performance

11. Tao Wang 11
Text Line Bounding boxes
Candidate spaces

12. Tao Wang 12
81.4 81.7
64
89
50
55
60
65
70
75
80
85
90
95
100
Yokobayashi et
al., 2006
Coates et al.,
2011
K.Wang et al.,
2011
Our Approach Human
83.9
62-way classification accuracy on ICDAR cropped characters
(on ICDAR-Sample
characters)
Accuracy(%)
Higher is
better
Classifier Performance

13. Tao Wang 13

14. Tao Wang 14
Char
Class
Sliding window position

15. Tao Wang 15
Word Recognition
Lexicon:
…
MAKE
SERIES
ESTATE
POKER
…
S E R I E S -5.45
7.82
-1.74
-9.02
max ∑

16. Tao Wang 16
76
82
90
62
84
57
73
70
40
50
60
70
80
90
100
ICDAR-WD-50 ICDAR-WD-FULL SVT-WD
K.Wang et al., 2011
Mishra, et al., 2012
Our approach
Cropped Word Recognition Accuracy
Accuracy(%)
Cropped Words Benchmarks
Higher is better

17. Tao Wang 17
…
…
Candidate spaces
generated by detector
max( )
j
j
M
Seg
M Seg
S



18. Tao Wang 18

19. Tao Wang 19
End-to-end text recognition results
0.72
0.76
0.7
0.74
0.68
0.72
0.51
0.67
0.38
0.46
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
ICDAR-5 ICDAR-20 ICDAR-50 ICDAR-FULL SVT
K.Wang et
al., 2011
Our
approach
F-Score
End-to-end Benchmarks
Higher is better

20. Tao Wang 20
Sample Output
Images from SVT

21. Tao Wang 21
Sample Output Images
from ICDAR-FULL

22. Tao Wang 22
max( )
max({ })
n c
m n c n

  -- “confidence margin”
PEOSTEL
PEOST
POST
POS
Hunspell
POSE
POST
PEOPLE
PISTOL
…
LEXICON
Suggested
Words
Our F-score: 0.38
Neumann and
Matas, 2010: 0.40
c

23. Tao Wang 23
• Learnt features + 2-layer CNN for+ character detection and classification
• Simple heuristics to build end-to-end scene text recognition system
• State-of-the-art performances on
- ICDAR cropped character classification
- ICDAR cropped word recognition
- Lexicon based end-to-end recognition on ICDAR and SVT
• Extensible to more general lexicon with off-the-shelf spelling checker
Conclusion

24. Tao Wang 24