A brief introduction to OCR (Optical character recognition)

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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

Engineering

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

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

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

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

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

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

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

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

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)

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IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.

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OCR application is developed with IMAQ Vision for LabVIEW software- developing tool and it uses a commercial digital camera from any android phone as image acquisition device.The proposed device will assist visually handicapped people in reading the printed text matter in English Language. It will look out for text signs which may be written text on printed books, newspapers, posters, etc and then live video frames of the text will be sent to labview for image processing and finally the recognised characters will be spoken.

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Optical Character Recognition (OCR) is the process which enables a system to without human intervention identifies the scripts or alphabets written into the users’ verbal communication. Optical Character identification has grown to be individual of the mainly flourishing applications of knowledge in the field of pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this paper we resolve and examine the hypothetical and numerical models of Optical Character Identification. The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR) techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on Optical Character detection or recognition has shown that the In Handwritten text there is no limitation lying on the script technique. Hand written correspondence is complicated to be familiar through due to diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of approaches of Optical Character Identification is discussed here all along through their achievement.

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I will discuss the state of the art of video understanding, particularly its research and applications at Facebook. I will focus on two active areas: multimodality and time. Video is naturally multi-modal, offering great possibility for content understanding while also opening new doors like unsupervised and weakly-supervised learning at scale. Temporal representation remains a largely open problem; while we can describe a few seconds of video, there is no natural representation for a few minutes of video. I will discuss recent progress, the importance of these problems for applications, and what we hope to achieve.

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A brief introduction to OCR (Optical character recognition)

1. Terry Taewoong Um fb.com/deeplearningtalk fb.com/terryum 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)

A brief introduction to OCR (Optical character recognition)

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