ICDAR 2023 Competition on Reading the Seal Title
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Abstract (arXiv)
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 from academia and industry including 28 submissions for Task 1 and 25 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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 16 references that are in the map. First posted on arXiv 24 Apr 2023.
Builds on
14 of 16 references.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025DianJin-OCR-R1: Enhancing OCR Capabilities via a Reasoning-and-Tool Interleaved Vision-Language Modelcited by 10
- 2025DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learningcited by 4
- 2025DKDS: A Benchmark Dataset of Degraded Kuzushiji Documents with Seals for Detection and Binarizationcited by 2
- 2026Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Datasetcited by 0
- 2026Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset28 May 2026
- 2025DKDS: A Benchmark Dataset of Degraded Kuzushiji Documents with Seals for Detection and Binarization12 Nov 2025
- 2025DianJin-OCR-R1: Enhancing OCR Capabilities via a Reasoning-and-Tool Interleaved Vision-Language Model18 Aug 2025
- 2025DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning5 Apr 2025