in this presentation, one can know the text extraction method called stroke width transform and its advantages, disadvantages and applications.

1. Detecting Text in Natural Scenes with Stroke Width
Transform
Presented by,
POOJA G N

2. Overview
• Introduction
• Steps involved in text detection algorithm
• Edge map
• Stroke width transform
• Finding letter candidates
• Grouping letter candidates
• Strength and weakness of SWT
• Results
• Applications
• References

3. Introduction
• With the increasing use of digital image capturing devices,
content-based image analysis techniques are receiving intensive
attention in recent years.
• As indicative marks in natural scene images, text information
provides brief and significant clues for many image-based
applications.
• We present a image operator that seeks to find the value of
stroke width for each image pixel, and demonstrate its use on
the task of text detection in natural images.

4. Introduction(contd.,)
Current text detection approaches can be roughly classified into three groups:
 Region-based approaches
This attempt to use similarity criterions of text, such as color, size, stroke
width, edge and gradient information, to gather pixels.
 Texture based approaches
This utilize distinct textural properties of text regions to extract candidate
sub-windows and the final outputs are formed by merging these sub-windows.
 Hybrid approaches
This take advantages of both region-based approaches which can closely
cover text regions and texture-based approaches which can estimate
coarse text location in scenes.

5. Steps involved in text detection algorithm

6. 1. Image(input)

7. 2. Edge map
 Here we use Canny Edge detection algorithm.
 The Canny edge detector is an edge detection operator that uses
a multi-stage algorithm to detect a wide range of edges in images.
Input image Edge detected image

8. 3. Stroke Width Transform
SWT is a local operator which calculates for each pixel the width of the most likely
stroke containing the pixel.
(a).
(b).
(c).
Figures shows the implementation of the SWT
where
(a) A typical stroke. The pixels of the stroke in
this example are darker than the background
pixels.
(b) p is a pixel on the boundary of the stroke.
Searching in the direction of the gradient at
p, leads to finding q, and the
corresponding pixel on the other side of the
stroke.
(c) Each pixel along the ray is assigned by the
minimum of its current value and the
found width of the stroke.

9. The rules to components are as follows:
• The variance of the stroke-width within a
component must not be too big.
• The aspect ratio of a component must be within a
small range of values, in order to reject long and
narrow components.
• Components whose size is too large or too small
will also be ignored.
4. Finding Letter Candidate

10. 5. Grouping letter candidates into regions of text
• Grouping the pixels into letter candidates based on their stroke width.
• The grouping of the image will be done by using a Connected Component algorithm.
• The image partition creates a set of connected components from an input
image, including both text characters and unwanted noises.
• We perform structural analysis of text strings to distinguish connected
components representing text characters from those representing noises.
• Assuming that a text string has at least three characters in alignment, we
develop two methods to locate regions containing text strings: adjacent
character grouping and text line grouping.

11. Grouping letter candidates into regions of text(contd.,)
• Group closely positioned letter candidates into regions of text.
• Filters out many falsely-identified letter candidates, and improves the
reliability of the algorithm results.
The rules to pair the letters are as follows:
• Two letter candidates should have similar
stroke width.
• The distance between letters must not
exceed three times the width of the wider
one.
• Characters of the same word are expected
to have a similar color; therefore we
compare the average color of the candidates
for pairing.

12. Resultant Image at each step of the algorithm

13. Strengths of SWT
• The SW Detector can detect letters of different languages (English, Hebrew, Arabic etc.)
• The text can be of varying sizes.
• The text can be of different orientation, including curvy text.
• Even handwriting can be detected.
Weakness of SWT
• Appearance of noise.
• Foliage resembles letters.
• Does not handle round and curved letters.
• Small and close letters tend to be grouped together in the SW labeling phase and these
groups may be dismissed in the ‘finding letter candidates’ phase.

14. Results

15. Applications
 Mobile text recognition
 Content-based web image search
 Automatic geocoding
 Robotic navigation
 License plate reading

16. References
1) Gili Werner ”Text Detection in Natural Scene with Stroke Width Transform”. ICBV,
February, 2013.
2) B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke
width transform,” in Computer Vision and Pattern Recognition(CVPR),Conference
on. IEEE, 2010.
3) Mr. Hemil A. Patel, Mrs. Kishori S. Shekokar, “Text Detection in Natural Scenes with
Stroke Width Transform”, [Patel, 3(11): November, 2014], ISSN: 2277-9655.
4) L. Neumann, J. Matas, “ A method for text localization and recognition in real-world
images”, ACCV, 2010.

17. Any queries?

18. Thank you