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CNBCC: cubic non-uniform B-spline closed curve for arbitrary shape text detection

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Abstract

With the development of deep learning, the performance and efficiency of text detection in natural scenes have been significantly improved. Due to the irregular geometric shape of natural scene text, it is challenging to detect text of arbitrary shape. Most of the existing methods are regression-based or segmentation-based methods. This paper presents an efficient framework to detect arbitrary shape text instances by combining regression-based and segmentation-based methods. Specifically, we use cubic non-uniform B-spline closed curve to fit the boundaries of arbitrary-shaped text instances. By adopting the anchor-free method as the regression detector to obtain the coordinates of B-spline curve control points, and using the segmentation method to obtain the knot vector value, our method not only uses the detection efficiency of regression method, but also combines the insensitivity of segmentation method to arbitrary shape text to improve the accuracy of text detection. Experiments on ICAR2015, CTW1500 and total-text benchmarks, including regular shape and arbitrary shape scene text in natural images, demonstrate the effectiveness of the proposed method.

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The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study.

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Authors and Affiliations

  1. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    Chao Zhu, Benshun Yi & Laigan Luo

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  1. Chao Zhu
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  2. Benshun Yi
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Correspondence to Benshun Yi.

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Zhu, C., Yi, B. & Luo, L. CNBCC: cubic non-uniform B-spline closed curve for arbitrary shape text detection. Vis Comput 40, 3023–3032 (2024). https://doi.org/10.1007/s00371-023-03005-7

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