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STW Text Detector

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

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  1. STW Text Detector Gili Werner

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

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

  4. 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

  5. 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

  6. The Stroke Width Transform • Finding letter candidates • Grouping the pixels into letter candidates based on their stroke width

  7. 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

  8. Results

  9. Results

  10. Strengths • 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

  11. Weaknesses • 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

  12. Questions?

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