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STW Text Detector. Gili Werner. Motivation. Detecting text in a natural scene is an important part of many Computer Vision tasks. Motivation.

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Stw text detector

STW Text Detector

Gili Werner


  • Detecting text in a natural scene is an important part of many Computer Vision tasks


  • For example, the performance of optical character recognition (OCR) algorithms can be highly improved by first identifying the regions of text in the image

Swt text detector
SWT Text Detector

  • In this project I attempted to create a powerful and reliable tool for detecting text regions in an image, by using the Stroke Width Transform (SWT)

    • grouping pixels together in an intelligent way, instead of looking for separating features of pixels

The stroke width transform
The Stroke Width Transform

3 major steps:

  • The stroke width transform

    • A stroke in the image is a continuous band of a nearly constant width

    • SWTis a local operator which calculates for each pixel the width of the most likely stroke containing the pixel

The stroke width transform1
The Stroke Width Transform

  • Finding letter candidates

    • Grouping the pixels into letter candidates based on their stroke width

The stroke width transform2
The Stroke Width Transform

  • Grouping letter candidates into regions of text

    • 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 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


  • Appearance of noise

    • Foliage resembles letters

  • Does not handle round and curved letters as well

  • Small and close letters tend to be grouped together in the SW labeling phase

    • These groups may be dismissed in the ‘finding letter candidates’ phase