Recently, digital transformation has been a topic that attracts a lot of attention all over the world, and it cannot be denied that AI is playing an important role in this field. OCR techniques have proven themselves to be an effective solution for this movement.

1. OCR techniques for
digital transformation
revolution
Quang Pham- R&D Unit, Sun-Asterisk Viet Nam
Vietnam Frontier Summit 2019

2. Why do you need
DX?
DX mean the transition from traditional
management to digital management.

3. ● Billions of documents from banks,
hospitals, and state offices are
created every day.
● The documents contain important
information for business operations.
● Most of the available documents
are paper documents
● Organizations that need to exploit
documents are not organizations
that issue documents
● For good exploitation, there should
be a system for digitizing
transformation
DX is an extremely important problem for many businesses.

4. How does the DX
change the world?
● Reduce storage costs, increase the ability to
search and process data
● Reduce operating costs through automation of
processes
● Reduce data entry time, validation time, increase
processing volume
● Increase customer satisfaction, reduce
processing time, reduce waiting time of
customers

5. Introduction to OCR techniques
Traditional OCR technologies and software
Architecture of OCR systems
Introduction to OCR techniques
Some traditional technologies and software
Introduce the important modules needed for an OCR system
Content01
02
03
Details of the important modules of OCR systems
04 Some important modules of OCR systems
Details abouts some important modules of OCR systems
05 Several case studies designed for OCR systems

6. Introduction to OCR techniques
● Extract information from PDF
documents/Scan form/Image
● Some document such as ID card, bill
form, diplomas, banks forms, certificates,
etc.

7. Traditional OCR technologies
and software
Software
Package/program Cloud API

8. Traditional OCR technologies
and software
● Difficult to integrate into other systems
● Quality depends a lot on the quality of the
input image
● Not available for specific documents such
as ID cards, etc.
● Do not handle handwriting cases especially
Vietnamese handwriting

9. Architecture of OCR systems
● Alignment document: crop, skew correction
● Image classification
● Image segmentation
● Text detection
● Text recognition
● Language model for post-preprocessing

10. Image alignment/Text
alignment
● Feature based alignment
● Document detection using
segmentation/object detection
● Text skew correction

11. Image classification
- Using Convolution neural network to
feature extraction, CNN architectures are
called base models.

12. Image segmentation
- Similar to image classification, image
segmentation using CNN to feature
extraction.
- Segmented image is the same size as
image input
- The network is trained using a pixel-wise
loss

13. Image segmentation
- Other architecture: Unet, Feature Pyramid
Network(FPN), Pyramid Scene Parsing
Network(PSPNet), Mask R-CNN, Deeplab,
etc.

14. Text detection
● Text detection CTPN?
● Object detection problem for text
● Some architecture for object detection:
R-CNN, Fast R-CNN, Faster R-CNN, Yolo,
SSD, etc.
● To understanding architecture: read Viblo
post
● Other framework can use: TensorFlow
Object Detection API

15. OCR: Text recognition
- Architecture used: CNN + RNN + CTC loss
- CNN model is based on your creativity
- Using attention model to improve accuracy
- Framework can use: Attention-OCR by
google research

16. OCR
Post-Processing
- Greedy search
- Lexicon searching
- Beam searching + lexicon searching + language model
- Word Beam Search

17. Differences from the traditional
approaches
● Use AI to identify writing, combined with
many pre-processing methods with
different inputs to increase accuracy
● Combine language model to increase
accuracy, automatically correct spelling
errors
● Use AI to create training data
● Fast processing speed, parallel computing
● Only extract important information
● It is possible to alert the manager when the
extracted data is not reliable enough

18. Case study 1
Crop and text
recognition

19. Case study 1
Image classify
Image segment
Image alignment
crop
Output:
48.829.600

20. Does anyone have any questions?
Or email at:
pham.huu.quang@sun-asterisk.com
THANKS!