STW Text DetectorGili Werner

MotivationDetecting text in a natural scene is an

important part of many Computer Vision tasks

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 

Results

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

Questions?