These are ppts from my Project Presentation. If there are any mistakes then please kindly ignore.

1. A Presentation
on
Self-Directing Text Detection and Removal from
Images with Smoothing
By
WAGH PRIYANKA DEELIP
Under the Guidance of
PROF. D. R. PATIL
Department of Computer Engineering
SES’s R.C.Patel Institute of Technology, Shirpur
Maharashtra State, India
2014-151

2. Outline
Introduction
Literature Survey
Problem Definition
Objectives
Methodology
Experimental Results
Conclusions
References
Publications
2

3. Introduction
Images consists of Text in various forms such as: logos,
subtitles, captions, banners etc.
Figure: Examples of Logos, captions, subtitles
3

4. Introduction Contd..
Drawback of having text in Image:
Occludes important portion of the Image and easy to index.
That’s why Text detection and removal system is required.
4

5. Introduction Contd..
The stages of Automatic Text detection and removal
scheme:
1) Self-directing Text detection
2) An Effective hole filling after the text removal [1].
Figure: Text Detection and Removal results
5

6. Introduction Contd..
Figure: Existing Architecture for Automatic Text Detection and
Removal from Image System
6

7. Introduction Contd..
An Automated Text Detection:
Computer system should answer:
Where is a text string in image?
Using such system text embedded in complex backgrounds
can be automatically detected.
7

8. Introduction Contd..
Figure: Basic Architecture for Automatic Text Detection from Image
System
8

9. Introduction Contd..
Applications of Automated Text Detection:
Optical Character Recognition.
Content-based video coding or document coding.
License/container plate recognition.
Text based image indexing.
Automated text removal system.
9

10. Introduction Contd..
An Automated Region filling:
A process of filling holes with surrounding region of image
without help of user.
Basic methods available for region filling:
1. Texture Synthesis: For large holes.
2. Inpainting: For small scratches.
Figure: Region filling
10

11. Introduction Contd..
Texture Synthesis:
A process of algorithmically constructing a large digital im-
age from a small digital sample image.
Figure: Texture Synthesis
11

12. Introduction Contd..
Inpainting:
Inpainting is the process of reconstructing lost or deterio-
rated parts of images and videos.
Figure: Inpainting
12

13. Introduction Contd...
Applications of Region filling:
Restoration of old photographs and damaged films.
Removal of superimposed texts.
Object Removal.
Occlusion or shadow removal.
Adding special effects.
Getting the clear visibility of original image while removing
unwanted elements of images for research purpose in insti-
tutes like NASA or defense DRDO.
To get clear view and gap filling of satellite images.
13

14. Literature Survey
Mohammad Khodadadi et. al. have proposed:
A method for text localization, extraction and inpainting.
Localization using: stroke filter and segmentation.
Extraction using: Background and text color estimation and
color histogram of candidate text blocks.
Inpainting: Based on matching algorithm and priority for in-
painting of pixel [1].
14

15. Literature Survey Contd..
J. Malobabic et. al. have proposed:
A method for text detection and localization for superimposed
video text using horizontal difference magnitude measure
and morphological processing.
Smoothing and multiple binarisation is used for enhancing
result.
The presented evaluation is based upon manually selected
best detection results.
Further research is essential to bring automaticity in this pro-
cess [2].
15

16. Literature Survey Contd..
Boris Epshtein et. al. have proposed:
Stroke width Transformation for text detection.
No need for multi-scale computation or scanning windows.
Reliable, Efficient and Language independent along with 15
times faster [3].
16

17. Literature Survey Contd..
Ali Mosleh et. al. have proposed:
A method for text detection using Feature vector.
Feature vectors composed of: Directionality of gradient of
text edges, High contrast with background, geometric prop-
erties of text components.
K-means clustering for text and non-text distinguishing.
A novel bandlet based edge detector for obtaining edges [4].
17

18. Literature Survey Contd..
Huizhong Chen et. al. have proposed:
A method to employ edge-enhanced Maximally stable ex-
tremal Regions as basic letter candidates.
Candidates filtering to exclude non-text objects by use of ge-
ometric and stroke width information.
Identification of text lines using letter pairing.
Dataset: ICDAR and mobile document database [5].
18

19. Literature Survey Contd..
Marcelo Bertalmio et. al. have proposed:
Method of digital automatic inpainting.
Fill-in: Isophote lines arriving at the regions’ boundaries are
completed inside.
Advantage: No need for user to specify where the novel in-
formation comes from [6].
19

20. Literature Survey Contd..
Lai-Man Po et. al. have proposed:
Multidirectional extrapolation hole filling method with com-
plex texture background.
Hole filling direction estimation using neighbor pixel’s texture
features.
Better virtual views synthesis with high quality depth map for
large hole fillings [8].
20

21. Problem Definition
Currently existing options for text detection and removal are
based on users and their technical skills to use those tools.
These tools and softwares consumes lot of time and ef-
forts utilization for text detection and as well as region filling.
Current systems are not at all common/non-technical user
friendly.
21

22. Objectives
To study and use the effects of smoothing on automatic text
detection system in order to improve the performance of
system.
To embed new inpainting method to bring more visually
plausible hole fillings.
To compare the results of smoothing based method, new
inpainting based method with existing methods.
22

23. Methodology
Text detection using text localization and extraction with smooth-
ing and Exemplar based Inpainting method. i.e. (TLES+EBI)
Main stages of the Automatic video Text detection and re-
moval system are as [1]:
1. Text Localization: to approximately detect text regions.
2. Text Extraction: extract more perfect text regions from un-
wanted regions.
3. Inpainting: Fill the holes generated using surrounding region
data.
23

24. Methodology Contd...
Modified architecture of text detection and removal to im-
prove performance of text detection:
Figure: 4 Modified architecture
24

25. Methodology Contd...
25

26. Methodology Contd...
What is Smoothing?
A process to create an approximating function that attempts:
To capture important patterns in the data.
To leave out noise or other fine-scale structures/rapid phe-
nomena.
Individual points in signal are reduced.
Points lower than adjacent points are increased leading to a
smoother signal.
26

27. Methodology Contd...
Effect of smoothing [9]:
Figure: 5 Original Image and Image after L0 Smoothing
27

28. Methodology Contd...
Why L0 gradient minimization?
Its effective method for sharpening edges [9]:
1. by increasing steepness of transition.
2. by eliminating low amplitude structures in statistical manner.
Doesn’t depend on local features but globally locates impor-
tant edges.
Retains primary color change by restricting drastic color change
of many pixels and reduces fractional diversities of patters
in image.
Characterizes and enhances fundamental image constituents.
28

29. Methodology Contd...
Figure: 5 Basic steps of L0 Gradient minimization Smoothing
29

30. Methodology Contd...
30

31. Methodology Contd...
Figure: Text Detection Algorithm [1]
31

32. Methodology Contd...
32

33. Methodology Contd...
Why Exemplar Based Inpainting?
Proved better visibly plausible results in field of inpainting
where NBMI lags.
Combines advantages of two methods:
1. Texture Synthesis: Filling large holes with use of large
region generation from sample texture.
2. Inpainting: Filling small scratches or gaps or holes using
diffusion.
Simultaneous texture and structure information propagation
achieved by single efficient algorithm.
Block based sampling provides computational efficiency [7].
33

34. Methodology Contd...
Working methodology [7]:
Figure: Exemplar based inpainting methodology
34

35. Implementation
Figure: Main Window
35

36. Implementation Contd...
Figure: Browse Window
36

37. Implementation Contd...
Figure: Text Detection Results
37

38. Implementation Contd...
Figure: Text Removal and Inpainting Results
38

39. Experimental Results
Text Detection Evaluation:
Figure: Detection Rate Evaluation
39

40. Experimental Results cont...
Comparison for text detection rate:
Figure: Text Detection Results
40

41. Experimental Results cont...
PSNR and MSE values for each method:
Figure: PSNR and MSE Result Table
41

42. Experimental Results cont...
Comparison for MSE of inpaiting outputs:
Figure: MSE Comparison
42

43. Experimental Results cont...
Comparison for PSNR of inpaiting outputs:
Figure: PSNR Comparison
43

44. Conclusions
The smoothing procedure can improve text detection rate
while decreasing false positive and false negative values.
In case of 5.4560% text image detection rate has been
achieved to 66.0444.
The exemplar based inpainting give more visually plausible
output than existing method.
The MSE decreases while PSNR values increases with
use of smoothing and exemplar based inpainting method in
text detection and removal system to 1.9235e-04 and
85.2899 respectively.
44

45. References
M. Khodadadi and A. Behrad, “Text Localization, Extraction and Inpainting in color Images", In proc. of Iranian
Conference on Electrical Engineering (ICEE2012), pp.1035-1040, May, 2012.
J. Malobabic and N. OŠConnor and N. Murphy and S. Marlow, “Automatic Detection and Extraction of Artificial
Text in Video", WIAMIS- 5th International Workshop on Image Analysis for Multimedia Interactive Services,
2004.
Boris Epshtein and Eyal Ofek and Yonatan Wexler, “Detecting text in natural scenes with stroke width trans-
form", In proc. of IEEE conference on Computer Vision and Pattern Recognition(CVPR), 2010, pp. 2963-2970,
June, 2010.
Ali Mosleh and Nizar Bouguila and A. Ben Hamza, “Image Text Detection Using a Bandlet-Based Edge De-
tector and Stroke Width Transform", In proc. of British Machine Vision Conference, pp. 63.1-63.12, 2012
Huizhong Chen and Sam S. Tsai and Georg Schroth and David M. Chen and Radek Grzeszczuk and Bernd
Girod, “Robust Text Detection in Natural Images with Edge-Enhanced Maximally Stable Extremal Regions",
In proc. of IEEE International Conference on Image Processing, Brussels, Sept. 2011.
Marcelo Bertalmio and Guillermo Sapiro and Vicent Caselles and Coloma Ballester, “Image Inpainting", In
proc. of Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp.
417-424, 2000.
A. Criminisi and P. Perez and K. Toyama, “Region Filling and Object Removal by Exemplar-Based Image
Inpainting", IEEE Transactions on Image Processsing, vol. 13, pp. 1200-1212, 2004.
Lai-Man Po and Shihang Zhang and Xuyuan Xu and Yuesheng Zhu, “A new multidirectional extrapolation
hole-filling method for Depth-Image-Based Rendering", In proc. of 18th IEEE International Conference on45

46. Publications
Priyanka Deelip Wagh and D. R. Patil, “Survey on Automatic Text Detection, Extraction and Removal from
video or images", In Proc. of National Conference on Emerging Trends in Computer Technology(NCETCT),
pp. 19-23, December 2014.
Priyanka Deelip Wagh and D. R. Patil, “Automatic Text Detection, Extraction and Removal from Video or
images", In Proc. of International Conference on Science and Technology 2K14, Indapur, Maharashtra, 2014.
Priyanka Deelip Wagh and D. R. Patil, “Text Detection and Removal from Image using Inpainting with Smooth-
ing", In Proc. of International Conference on Pervasive Computing 2015, Sinhagad college of Engineering,
Pune, 8-10 January 2015.
Priyanka Deelip Wagh and D. R. Patil, “Self-directing Superimposed Text Detection and Removal from images
using Inpainting for Region Filling with Smoothing", In proc. of International Conference on Advances in En-
gineering and Technology, Anjuman College of Engineering and Technology, Sadar, Nagpur, 25-26 February
2015.
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47. Thank You !!!
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