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Real-Time Accurate Text Detection with Adaptive Double Pyramid Network

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Abstract

Segmentation-based methods have been widely adopted in scene text detection recently, for they could more accurately predict the shape of various scene text at pixel-level than other methods. However, complicated feature aggregation or label assignment algorithms used in current segmentation-based methods would significantly decrease the detection speed during the improving of accuracy. In this paper, we present an Adaptive Double Pyramid Network (ADPNet) for real-time detection of arbitrary-shaped text, which sets a Double Feature Enhancement Pyramid using Packet Downsampling Units (PDUnits) to enhance feature maps with a minimal amount of processing. The performance of ADPNet is validated on three benchmark datasets, and it shows that ADPNet obtains state-of-the-art performance in both speed and accuracy. Specifically, the proposed network achieves an F-measure of 85.7% while running at 40.5 fps on the ICDAR2015 dataset.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61404083) and State Key Laboratory of ASIC & System (2021KF010).

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  1. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Weina Zhou & Wanyu Song

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  1. Weina Zhou
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  2. Wanyu Song
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Correspondence to Weina Zhou.

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Zhou, W., Song, W. Real-Time Accurate Text Detection with Adaptive Double Pyramid Network. Neural Process Lett 55, 5055–5067 (2023). https://doi.org/10.1007/s11063-022-11080-5

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