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Scene Text Detection archive 2025-07-28

ICDAR 2013 Benchmark (Scene Text Detection)

16 rows 13 with code listed 4 metrics Dataset page

Scene Text Detection is a computer vision task that involves automatically identifying and localizing text within natural images or videos. The goal of scene text detection is to develop algorithms that can robustly detect and and label text with bounding boxes in uncontrolled and complex environments, such as street signs, billboards, or license plates.

Source: ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection

The archive carries no text for this table; the description above is the archive's text for the task Scene Text Detection. archive 2025-07-28

Over time archive 2025-07-28

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Direction inferred from the metric name, not from the archive: Precision (higher is better), Recall (higher is better). Not inferred (points only, no best-so-far line): F-Measure, H-Mean. Points are placed at the row's paper date; 16 of 16 rows carry one.

Results archive 2025-07-28

Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.

Paper Code Ran Syntology Report
1 TextFuseNet (ResNeXt-101) 94.61%97.2792.09 – Paper Code 2020 linked, not harvested report
2 SPCNET 92.1%93.890.5 – Paper Code 2018 linked, not harvested report
3 Mask TextSpotter 91.7%9588.6 – Paper Code 2018 linked, not harvested report
4 WordSup (VGG16-synth-icdar) 90.34%93.3487.53 – Paper – 2017 no code linked report
5 STN-OCR 90.3% – Paper Code 2017 linked, not harvested report
6 PixelLink+VGG16 2s MS 88.1%88.687.5 – Paper Code 2018 linked, not harvested report
7 Corner Localization (multi-scale) 88%9284.4 – Paper Code 2018 linked, not harvested report
8 TextBoxes++_MS 88%%9184 – Paper Code 2018 linked, not harvested report
9 Corner-based Region Proposals 87.6%%91.983.9 – Paper Code 2018 linked, not harvested report
10 SSTD 87%8886 – Paper Code 2017 linked, not harvested report
11 SegLink 85.3%87.783 – Paper Code 2017 1 of 1 ran · 0 unverified report
12 Gupta et al. 83.0%92.075.5 – Paper Code 2016 0 of 6 ran · 6 unverified report
13 USM (COCO TS + ICDAR–2013) 80.40% – Paper Code 2019 linked, not harvested report
14 Neumann et al. * 77.1%81.872.4 – Paper – 2015 no code linked report
15 Jaderberg et al. 76.8%88.567.8 – Paper – 2014 no code linked report
16 CRAFT 97.493.195.2 – Paper Code 2019 6 of 40 ran · 34 unverified report

All 16 rows shown. 16 link to a paper page on this site; 0 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28

Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 3 rows have a graph line, from 3 distinct papers; 2 rows (2 papers) have at least one sample that ran. Counting each paper once: Syntology ran 7 of 47 samples; 40 unverified. Separately, 5 of those 47 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.

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