Welcome to our first

Computer Vision Meetup

Sponsored by

Daniel Albertini Technical Director & Co-Founder

daniel@anyline.io

Anyline - a product of 9yards GmbH

Zirkusgasse 13/2b

1020 Wien

Agenda

- Overview Talk about different text detection strategies. - Feedback about possible future Meetup topics. - Get-together, discuss and beer.

Text Detection Strategies Overview

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.

SWT (Stroke Width Transformation)

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

SWT (Stroke Width Transformation)

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).

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.

MSER

Threshold value: 10 45 75

105 135 165

MSER (Maximally Stable Extremal Regions)

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).

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

The End

Sources

SWT: http://research.microsoft.com/pubs/149305/1509.pdf MSER: http://www.icg.tugraz.at/pub/pubobjects/docvpr2006