These slides include the answers for the following questions: - What is OCR? - Why do we need it? - Why is it difficult? - Comparison between OCR & object detections - Three approaches for text localization - Three approaches for text recognition Videos are also available from the below: (Korean) https://youtu.be/ckRFBl_XWFg (English) coming soon [Reference] Hwalsuk Lee, https://www.slideshare.net/deview/111-ai

1. Terry Taewoong Um
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사진 속 글자를 읽어주는
Optical Character
Recognition (OCR)
42

2. What is OCR?
• Optical Character Recognition (OCR)
Reading typed/printed/handwritten characters
from image sources
Speech
Recognition

3. What is OCR?
• Optical Character Recognition (OCR)
Reading typed/printed/handwritten characters
from image sources
OCR

4. Why OCR?
characters in the
computer
characters in the
physical world
A

5. Why OCR?
characters in the
computer
characters in the
physical world
Difficult because of the large variations!
(font, size, shape, location, noise, ...)

6. OCR vs Object detection
Text
Localization
Text
Recognition
• OCR
• Object detection
Object
Localization
Object
Recognition
Detect the bounding
boxes that enclose text Read it
• OCR is more challenging than object detection due to
- various aspect (W:H) ratio - large distortions
- confusion w/ textures (‘I’, ‘T’)
- few pretrained models- high density
- various languages

7. Text Localization Text
Localization
Text
Recognition
이활석, https://www.slideshare.net/deview/111-ai
regression-based
(like object detection)
end-to-end
[Textboxes, Liao et al., AAAI2017] [PixelLink, Deng et al., AAAI2018]
classification-based
(like semantic segmentation)
[FOTS, Liu et al., CVPR2018]
simultaneous local+recog
# of
papers
training unstable stable

8. Text Recognition Text
Localization
Text
Recognition
Connectionist
Temporal
Classification
r EOSpt i
t pi<GO> r
Attention
# of
papers
speed
rarely used
accuracy

9. OCR + Translation = SmartLens
Text
Localization
Text
Recognition
Machine
translation
• What you need to know is
- Machine learning basics - Neural network basics
- Convolutional Neural Networks (+ advanced topics)
- Recurrent Neural Networks (+ advanced topics)