Abstract
Detecting arbitrary oriented text in scene and license plate images is challenging due to multiple adverse factors caused by images of diversified applications. This paper proposes a novel idea of extracting Cloud of Line Distribution (COLD) for the text candidates given by Extremal regions (ER). The features extracted by COLD are fed to Random forest to label character components. The character components are grouped according to probability distribution of nearest neighbor components. This results in text line. The proposed method is demonstrated on standard database of natural scene images, namely ICDAR 2015, video images, namely ICDAR 2015 and license plate databases. Experimental results and comparative study show that the proposed method outperforms the existing methods in terms of invariant to rotations, scripts and applications.
W. Wang and Y. Wu indicates equal contribution.
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Acknowledgements
This work was supported by the Natural Science Foundation of China under Grant 61672273, Grant 61272218, and Grant 61321491, by the Science Foundation for Distinguished Young Scholars of Jiangsu under Grant BK20160021, by the Science Foundation of Jiangsu under Grant BK20170892, by the Fundamental Research Funds for the Central Universities under Grant 2013/B16020141 and by the open Project of the National Key Lab for Novel Software Technology in NJU under Grant KFKT2017B05.
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Wang, W., Wu, Y., Palaiahnakote, S., Lu, T., Liu, J. (2018). Cloud of Line Distribution for Arbitrary Text Detection in Scene/Video/License Plate Images. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_41
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