Abstract
Reading seal title text is a challenging task due to the variable shapes of seals, curved text, background noise, and overlapped text. However, this important element is commonly found in official and financial scenarios, and has not received the attention it deserves in the field of OCR technology. To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2). We constructed a dataset of 10,000 real seal data, covering the most common classes of seals, and labeled all seal title texts with text polygons and text contents. The competition opened on 30th December, 2022 and closed on 20th March, 2023. The competition attracted 53 participants and received 135 submissions from academia and industry, including 28 participants and 72 submissions for Task 1, and 25 participants and 63 submissions for Task 2, which demonstrated significant interest in this challenging task. In this report, we present an overview of the competition, including the organization, challenges, and results. We describe the dataset and tasks, and summarize the submissions and evaluation results. The results show that significant progress has been made in the field of seal title text reading, and we hope that this competition will inspire further research and development in this important area of OCR technology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bautista, D., Atienza, R.: Scene text recognition with permuted autoregressive sequence models. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13688, pp. 178–196. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19815-1_11
Chng, C.K., et al.: ICDAR 2019 robust reading challenge on arbitrary-shaped text - RRC-art. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1571–1576 (2019)
Chng, C.K., et al.: ICDAR 2019 robust reading challenge on arbitrary-shaped text-RRC-art. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1571–1576. IEEE (2019)
Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2315–2324 (2016)
He, K., Gkioxari, G., Dollár, P., Girshick, R.B.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42, 386–397 (2017)
Li, M., et al.: TrOCR: transformer-based optical character recognition with pre-trained models. arXiv abs/2109.10282 (2021)
Liao, M., Zou, Z., Wan, Z., Yao, C., Bai, X.: Real-time scene text detection with differentiable binarization and adaptive scale fusion. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 919–931 (2022)
Liu, X., et al.: ICDAR 2019 robust reading challenge on reading Chinese text on signboard. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1577–1581 (2019)
Liu, Y., Chen, H., Shen, C., He, T., Jin, L., Wang, L.: ABCNet: real-time scene text spotting with adaptive bezier-curve network. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9806–9815 (2020)
Liu, Y., Jin, L., Zhang, S., Luo, C., Zhang, S.: Curved scene text detection via transverse and longitudinal sequence connection. Pattern Recogn. 90, 337–345 (2019)
Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: transformer for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7262–7272 (2021)
Strudel, R., Pinel, R.G., Laptev, I., Schmid, C.: Segmenter: transformer for semantic segmentation. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7242–7252 (2021)
Sun, Y., et al.: ICDAR 2019 competition on large-scale street view text with partial labeling - RRC-LSVT. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1557–1562 (2019)
Wang, W., et al.: Tpsnet: reverse thinking of thin plate splines for arbitrary shape scene text representation. In: Proceedings of the 30th ACM International Conference on Multimedia (2021)
Wang, W., et al.: Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 8439–8448 (2019)
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 418–434 (2018)
Zhang, W., Pang, J., Chen, K., Loy, C.C.: K-Net: towards unified image segmentation. In: NeurIPS (2021)
Acknowledgements
This competition is supported by the National Natural Science Foundation of China (No. 62225603, No. 62206103, No. 62206104). The organizers thank Sergi Robles and the RRC web team for their tremendous support on the registration, submission and evaluation jobs.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, W. et al. (2023). ICDAR 2023 Competition on Reading the Seal Title. 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_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-41679-8_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-41678-1
Online ISBN: 978-3-031-41679-8
eBook Packages: Computer ScienceComputer Science (R0)
