Integrating multiple character proposals for robust scene text extraction

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Text contained in scene images provides the semantic context of the images. For that reason, robust extraction of text regions is essential for successful scene text understanding. However, separating text pixels from scene images still remains as a challenging issue because of uncontrolled lighting conditions and complex backgrounds. In this paper, we propose a two-stage conditional random field (TCRF) approach to robustly extract text regions from the scene images. The proposed approach models the spatial and hierarchical structures of the scene text, and it finds text regions based on the scene text model. In the first stage, the system generates multiple character proposals for the given image by using multiple image segmentations and a local CRF model. In the second stage, the system selectively integrates the generated character proposals to determine proper character regions by using a holistic CRF model. Through the TCRF approach, we cast the scene text separation problem as a probabilistic labeling problem, which yields the optimal label configuration of pixels that maximizes the conditional probability of the given image. Experimental results indicate that our framework exhibits good performance in the case of the public databases.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2013-11
Language
English
Article Type
Article
Keywords

OBJECT DETECTION; IMAGES; COLOR; RECOGNITION; FEATURES

Citation

IMAGE AND VISION COMPUTING, v.31, no.11, pp.823 - 840

ISSN
0262-8856
DOI
10.1016/j.imavis.2013.08.007
URI
http://hdl.handle.net/10203/188611
Appears in Collection
CS-Journal Papers(저널논문)
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