Robust outdoor text detection using text intensity and shape features

Recognizing texts from camera images is a known hard problem because of the difficulties in text segmentation from the varied and complicated backgrounds. In this paper, we propose an algorithm that employs two novel filters and a basic component-based text detection framework. The framework uses th...

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Veröffentlicht in:2008 19th International Conference on Pattern Recognition S. 1 - 4
Hauptverfasser: Zongyi Liu, Sarkar, S.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2008
Schlagworte:
Cameras
Filters
Geometry
Histograms
Image databases
Image recognition
Image segmentation
Robustness
Shape
Text recognition
ISBN:9781424421749, 1424421748
ISSN:1051-4651
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Abstract Recognizing texts from camera images is a known hard problem because of the difficulties in text segmentation from the varied and complicated backgrounds. In this paper, we propose an algorithm that employs two novel filters and a basic component-based text detection framework. The framework uses the Niblack algorithm to threshold images and groups components into regions with commonly used geometry features. The intensity filter considers the overlap between the intensity histogram of a component and that of its adjoining area. For non-text regions, we have found that this overlap is large, and so we can prune out components with large values of this measure. The shape filter, on the other hand, deletes regions whose constituent components come from a same object, as most words consist of different characters. The proposed method is evaluated with the text locating database with 249 images used in the ICDAR2003 robust reading competition. The result shows that the algorithm is robust to both indoor images and outdoor images, even for the images of complex background, which usually is a hard factor to overcome for traditional component-based algorithms. In terms of performance statistics, we tested the algorithm on the ICDAR 2003 challenge experiment, and the algorithm achieves 66% precision rate (p), 46% recall rate (r), and 54% the combined rate ( f ), which is the best reported in the literature on this dataset.
AbstractList Recognizing texts from camera images is a known hard problem because of the difficulties in text segmentation from the varied and complicated backgrounds. In this paper, we propose an algorithm that employs two novel filters and a basic component-based text detection framework. The framework uses the Niblack algorithm to threshold images and groups components into regions with commonly used geometry features. The intensity filter considers the overlap between the intensity histogram of a component and that of its adjoining area. For non-text regions, we have found that this overlap is large, and so we can prune out components with large values of this measure. The shape filter, on the other hand, deletes regions whose constituent components come from a same object, as most words consist of different characters. The proposed method is evaluated with the text locating database with 249 images used in the ICDAR2003 robust reading competition. The result shows that the algorithm is robust to both indoor images and outdoor images, even for the images of complex background, which usually is a hard factor to overcome for traditional component-based algorithms. In terms of performance statistics, we tested the algorithm on the ICDAR 2003 challenge experiment, and the algorithm achieves 66% precision rate (p), 46% recall rate (r), and 54% the combined rate ( f ), which is the best reported in the literature on this dataset.
Author Zongyi Liu
Sarkar, S.
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  organization: Dept. Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL
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Snippet Recognizing texts from camera images is a known hard problem because of the difficulties in text segmentation from the varied and complicated backgrounds. In...
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SubjectTerms Cameras
Filters
Geometry
Histograms
Image databases
Image recognition
Image segmentation
Robustness
Shape
Text recognition
Title Robust outdoor text detection using text intensity and shape features
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