Abstract
Text detection on scene images has increasingly gained a lot of interests, especially due to the increase of wearable devices. However, the devices often acquire low resolution images, thus making it difficult to detect text due to noise. Notable method for detection in low resolution images generally utilizes many features which are cleverly integrated and cascaded classifiers to form better discriminative system. Those methods however require a lot of hand-crafted features and manually tuned, which are difficult to achieve in practice. In this paper, we show that the notable cascaded method is equivalent to a Convolutional Neural Network (CNN) framework to deal with text detection in low resolution scene images. The CNN framework however has interesting mutual interaction between layers from which the parameters are jointly learned without requiring manual design, thus its parameters can be better optimized from training data. Experiment results show the efficiency of the method for detecting text in low resolution scene images.
The original version of this chapter was revised: Co-author name has been deleted. The erratum to this chapter is available at DOI: 10.1007/978-3-319-51281-5_65
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07 April 2017
The updated original online version for these chapter can be found at DOI: 10.1007/978-3-319-51281-5_37
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Acknowledgements
The authors would like to thank Pusat Penelitian dan Pengabdian Masyarakat (P3M) of Politeknik Elektronika Negeri Surabaya (PENS) for supporting this research by Local Research Funding FY 2016.
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Risnumawan, A., Sulistijono, I.A., Abawajy, J. (2017). Text Detection in Low Resolution Scene Images Using Convolutional Neural Network. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_37
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DOI: https://doi.org/10.1007/978-3-319-51281-5_37
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