Abstract
Benefit from the development of deep neural networks, scene text detectors have progressed rapidly over the past few years and achieved outstanding performance on several standard benchmarks. However, most existing methods adopt quadrilateral bounding boxes to represent texts, which are usually inadequate to deal with multi-shaped texts such as the curved ones. To keep consist detection performance on both quadrilateral and curved texts, we present a novel representation, i.e., text kernel, for multi-shaped texts. On the basis of text kernel, we propose a simple yet effective scene text detection method, named as TK-Text. The proposed method consists of three steps, namely text-context-aware network, segmentation map generation and text kernel based post-clustering. During text-context-aware network, we construct a segmentation-based network to extract feature map from natural scene images, which are further enhanced with text context information extracted from an attention scheme TKAB. In segmentation map generation, text kernels and rough boundaries of text instances are segmented based on the enhanced feature map. Finally, rough text instances are gradually refined to generate accurate text instances by performing clustering based on text kernel. Experiments on public benchmarks including SCUT-CTW1500, ICDAR 2015 and ICDAR 2017 MLT demonstrate that the proposed method achieves competitive detection performance comparing with the existing methods.
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Acknowledgment
This work is supported by the Natural Science Foundation of China under Grant 61672273 and Grant 61832008, and Scientific Foundation of State Grid Corporation of China (Research on Ice-wind Disaster Feature Recognition and Prediction by Few-shot Machine Learning in Transmission Lines), and National Key R&D Program of China under Grant 2018YFC0407901, the Natural Science Foundation of China under Grant 61702160, the Science Foundation of Jiangsu under Grant BK20170892.
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Song, X., Wu, Y., Wang, W., Lu, T. (2020). TK-Text: Multi-shaped Scene Text Detection via Instance Segmentation. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_17
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