Gili Werner. Motivation Detecting text in a natural scene is an important part of many Computer...
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Transcript of Gili Werner. Motivation Detecting text in a natural scene is an important part of many Computer...
MotivationFor 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 DetectorIn 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 Transform3 major steps: 1. The stroke width transform
A stroke in the image is a continuous band of a nearly constant width
SWT is a local operator which calculates for each pixel the width of the most likely stroke containing the pixel
The Stroke Width Transform2. Finding letter candidates
Grouping the pixels into letter candidates based on their stroke width
The Stroke Width Transform3. 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
StrengthsThe SW Detector can detect letters of
different languages (English, Hebrew, Arabic etc.)
The text can be of varying sizesThe text can be of different orientation
Including curvy textEven handwriting can be detected
WeaknessesAppearance of noise
Foliage resembles lettersDoes not handle round and curved letters as
wellSmall and close letters tend to be grouped
together in the SW labeling phaseThese groups may be dismissed in the ‘finding
letter candidates’ phase