Text Detection Strategies
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Transcript of Text Detection Strategies
![Page 1: Text Detection Strategies](https://reader031.fdocuments.in/reader031/viewer/2022021813/587080951a28ab57368b6451/html5/thumbnails/1.jpg)
Welcome to our first
Computer Vision Meetup
Sponsored by
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Daniel Albertini Technical Director & Co-Founder
Anyline - a product of 9yards GmbH
Zirkusgasse 13/2b
1020 Wien
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Agenda
- Overview Talk about different text detection strategies. - Feedback about possible future Meetup topics. - Get-together, discuss and beer.
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Text Detection Strategies Overview
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SWT (Stroke Width Transformation)
Computes per pixel the most likely stroke width containing the pixel. Steps: - Compute Edge Map of image. - Compute X & Y Gradient Map. - Calculate Ray from every edge pixel with
the direction from the gradient maps. - Set the value of the pixels of the ray to
the min of current value and ray length. - Group neighbor pixels with similar
stroke width together to find letter candidates.
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SWT (Stroke Width Transformation)
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SWT Rejecting connected components strategies: - Variance of the stroke width. - Aspect ratio.
- Too large & too small components - Components which are clearly not part of a
word / text line
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SWT (Stroke Width Transformation)
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SWT (Stroke Width Transformation)
Advantages: - Is able to accurately detect
text in different sizes, styles, colors.
- Can detect text independent of perspective and rotation.
- First step of SWT is a good all-rounder thresholding method for images with text.
Disadvantages: - Relatively slow performance
(edge & gradient maps). - Needs information if text or
background is darker (in the grayscale image).
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MSER (Maximally Stable Extremal Regions)
Blob detection method suitable for detecting character features. This method detects regions which are considered stable over a large range of threshold values.
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MSER
Threshold value: 10 45 75
105 135 165
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MSER (Maximally Stable Extremal Regions)
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MSER (Maximally Stable Extremal Regions)
Advantages: - Is able to accurately detect
text in different sizes, styles, colors.
- Can detect text independent of perspective and rotation.
- Good performance.
Disadvantages: - Sensible against blur. - No binary image as an output
(thresholding for OCR still needed).
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ER Variation for text detection
Sequential classifier trained for character detection instead of maximum region Advantages: - Only Character regions will be found. No need for analyzing and rejecting
components.
Disadvantages: - Needs training for different font or character types - Slower performance
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The End
Sources
SWT: http://research.microsoft.com/pubs/149305/1509.pdf MSER: http://www.icg.tugraz.at/pub/pubobjects/docvpr2006