Abstract
In this paper, we propose an approach to scene text detection that leverages both the appearance and consensus of connected components. A component appearance is modeled with an SVM based dictionary classifier and the component consensus is represented with color and spatial layout features. Responses of the dictionary classifier are integrated with the consensus features into a discriminative model, where the importance of features is determined with a text level training procedure. In text detection, hypotheses are generated on component pairs and an iterative extension procedure is used to aggregate hypotheses into text objects. In the detection procedure, the discriminative model is used to perform classification as well as control the extension. Experiments show that the proposed approach reaches the state of the art in both detection accuracy and computational efficiency, and in particularly, it performs best when dealing with low-resolution text in clutter backgrounds.
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Notes
- 1.
“Connected component” is shorted as “component” in the followings.
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Acknowledgement
The partial support of this research by DARPA through BBN/ DARPA Award HR0011-08-C-0004 under subcontract 9500009235, the US Government through NSF Award IIS-0812111 is gratefully acknowledged.
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Ye, Q., Doermann, D. (2014). Scene Text Detection via Integrated Discrimination of Component Appearance and Consensus. In: Iwamura, M., Shafait, F. (eds) Camera-Based Document Analysis and Recognition. CBDAR 2013. Lecture Notes in Computer Science(), vol 8357. Springer, Cham. https://doi.org/10.1007/978-3-319-05167-3_4
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