Abstract
Scene text detection has achieved great progress recently, however, it is challenging to detect arbitrary shaped text in the scene images with complex background, especially for those unobvious and long texts. To tackle this issue, we propose an effective text detection network, termed TextBFA, strengthening the text feature by aggregating high-level semantic features. Specifically, we first adopt a bidirectional feature aggregation network to propagate and collect information on feature maps. Then, we exploit a bilateral decoder with lateral connection to recover the low-resolution feature maps for pixel-wise prediction. Extensive experiments demonstrate the detection effectiveness of the proposed method on several benchmark datasets, especially on inconspicuous text detection.
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This research is supported by the National Natural Science Foundation of China under No. 62106247.
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Xu, H., Wang, QF., Li, Z., Shi, Y., Zhou, XD. (2024). TextBFA: Arbitrary Shape Text Detection with Bidirectional Feature Aggregation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1962. Springer, Singapore. https://doi.org/10.1007/978-981-99-8132-8_28
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DOI: https://doi.org/10.1007/978-981-99-8132-8_28
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