Curved Text Detection

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-25.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (14 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• EAST: An Efficient and Accurate Scene Text Detector
  11 Apr 2017 31 repositories listed Syntology ran 5 of 6 samples · 1 unverified · 1 pointer-only (licence)
  Previous approaches for scene text detection have already achieved promising performances across various benchmarks.
• Shape Robust Text Detection with Progressive Scale Expansion Network
  7 Jun 2018 9 repositories listed Syntology ran 5 of 24 samples · 19 unverified · 2 pointer-only (licence)
  To address these problems, we propose a novel Progressive Scale Expansion Network (PSENet), designed as a segmentation-based detector with multiple predictions for each text instance.
• Detecting Oriented Text in Natural Images by Linking Segments
  19 Mar 2017 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  It achieves an f-measure of 75.
• TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes
  4 Jul 2018 3 repositories listed Syntology ran 1 of 6 samples · 5 unverified
  Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance records on various standard benchmarks.
• Moving Object Detection for Event-based vision using Graph Spectral Clustering
  30 Sep 2021 1 repository listed
  However, these advantages come at a high cost, as the event camera data typically contains more noise and has low resolution.
• MTRNet: A Generic Scene Text Eraser
  11 Mar 2019 1 repository listed
  Developing such a generic text eraser for real scenes is a challenging task, since it inherits all the challenges of multi-lingual and curved text detection and inpainting.
• Sliding Line Point Regression for Shape Robust Scene Text Detection
  30 Jan 2018 1 repository listed
  Specifically, we first generate the smallest rectangular box including the text with region proposal network (RPN), then isometrically regress the points on the edge of text by using the vertically and horizontally…
• Detecting Curve Text in the Wild: New Dataset and New Solution
  6 Dec 2017 1 repository listed
  To raise the concerns of reading curve text in the wild, in this paper, we construct a curve text dataset named CTW1500, which includes over 10k text annotations in 1, 500 images (1000 for training and 500 for testing).
• Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
  28 Oct 2017 1 repository listed
  Text in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR2013 and MSRA-TD500.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-25.