Abstract
Text information in natural scene images is important for various kinds of applications. In this paper a novel method based on stroke width to detect text in unconstrained natural scene images is proposed. Firstly, we use the stroke width transform to generate a rough estimation of stroke width map, then use K-Means clustering and the elbow method to find some specific stroke width values that are both dominant and consistent. Secondly, in order to generate better edge detection and gradient direction results we use these specific stroke width values as the size parameters in the superpixel algorithm to generate smooth and uniform region boundaries. Finally, we try to refine the stroke width map and recover valid edge pixels by applying stroke width regularized constraints on the improved edge detection and gradient direction results computed from these region boundaries. Our method was evaluated on three benchmark datasets: ICDAR 2005, 2011 and 2013, and the experimental results show that it achieves state-of-the-art performance.
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Liu, S., Zhou, Y., Zhang, Y., Wang, Y., Lin, W. (2014). Text Detection in Natural Scene Images with Stroke Width Clustering and Superpixel. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, CK., Huet, B., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham. https://doi.org/10.1007/978-3-319-13168-9_13
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DOI: https://doi.org/10.1007/978-3-319-13168-9_13
Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-13168-9
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