Robust text detection in natural images with edge-enhanced Maximally Stable Extremal Regions

Detecting text in natural images is an important prerequisite. In this paper, we propose a novel text detection algorithm, which employs edge-enhanced Maximally Stable Extremal Regions as basic letter candidates. These candidates are then filtered using geometric and stroke width information to excl...

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Bibliographic Details
Published in2011 18th IEEE International Conference on Image Processing pp. 2609 - 2612
Main Authors Chen, H., Tsai, S. S., Schroth, G., Chen, D. M., Grzeszczuk, R., Girod, B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2011
Subjects
Conferences
connected component analysis
Detection algorithms
Feature extraction
Image edge detection
maximally stable extremal regions
Robustness
Text detection
Transforms
Visualization
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Summary:Detecting text in natural images is an important prerequisite. In this paper, we propose a novel text detection algorithm, which employs edge-enhanced Maximally Stable Extremal Regions as basic letter candidates. These candidates are then filtered using geometric and stroke width information to exclude non-text objects. Letters are paired to identify text lines, which are subsequently separated into words. We evaluate our system using the ICDAR competition dataset and our mobile document database. The experimental results demonstrate the excellent performance of the proposed method.
ISBN:1457713047
9781457713040
ISSN:1522-4880
DOI:10.1109/ICIP.2011.6116200