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Neural Processing Letters
Erschienen in: Neural Processing Letters 4/2023

17.11.2022

Real-Time Accurate Text Detection with Adaptive Double Pyramid Network

verfasst von: Weina Zhou, Wanyu Song

Erschienen in: Neural Processing Letters | Ausgabe 4/2023

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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.
Vorheriger Artikel Variance-Constrained Resilient State Estimation for Time-Varying Neural Networks with Random Saturation Observation Under Uncertain Occurrence Probability
Nächster Artikel Further Results on Fixed-Time Cluster Synchronization of Coupled Neural Networks

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Metadaten
Titel
Real-Time Accurate Text Detection with Adaptive Double Pyramid Network
verfasst von
Weina Zhou
Wanyu Song
Publikationsdatum
17.11.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 4/2023
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-11080-5

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