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DQ-DETR: Dynamic Queries Enhanced Detection Transformer for Arbitrary Shape Text Detection

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Document Analysis and Recognition - ICDAR 2023 (ICDAR 2023)

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Abstract

We propose a new Transformer-based text detection model, named Dynamic Queries enhanced DEtection TRansformer (DQ-DETR), to detect arbitrary shape text instances from images with high localization accuracy. Unlike previous Transformer-based methods which take all control points on the boundaries/center-lines of all text instances as the queries of each Transformer decoder layer, we extend the query set for each decoder layer gradually, allowing the DQ-DETR to achieve higher localization accuracy by detecting control points for each text instance progressively. Specifically, after refining the positions of existing control points from the preceding decoder layer, each decoder layer further appends a new point on each side of each center-line segment, which are input to the next decoder layer as additional queries for detecting new control points. As offsets from the new control points to the added reference points are small, their positions can be predicted more precisely, leading to higher center-line detection accuracy. Consequently, our DQ-DETR achieves state-of-the-art performance on five public text detection benchmarks, including MLT2017, Total-Text, CTW1500, ArT and DAST1500.

This work was done when Jiawei Wang was an intern in MMI Group, Microsoft Research Asia, Beijing, China.

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Notes

  1. 1.

    https://github.com/facebookresearch/detectron2.

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Authors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Chixiang Ma, Lei Sun, Jiawei Wang & Qiang Huo

  2. Department of EEIS, University of Science and Technology of China, Hefei, China

    Jiawei Wang

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  1. Chixiang Ma
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  2. Lei Sun
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  3. Jiawei Wang
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  4. Qiang Huo
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Correspondence to Chixiang Ma .

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Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. Adobe, College Park, MN, USA

    Rajiv Jain

  3. Osaka Metropolitan University, Osaka, Japan

    Koichi Kise

  4. Rochester Institute of Technology, Rochester, NY, USA

    Richard Zanibbi

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Ma, C., Sun, L., Wang, J., Huo, Q. (2023). DQ-DETR: Dynamic Queries Enhanced Detection Transformer for Arbitrary Shape Text Detection. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14188. Springer, Cham. https://doi.org/10.1007/978-3-031-41679-8_14

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