Model card
CRAFT.
UnknownunknownUnknown paramsUnknown
Imported from Papers With Code
§ 01 · Benchmarks
Every benchmark CRAFT has a recorded score for.
| # | Benchmark | Area · Task | Metric | Value | Rank | Date | Source |
|---|---|---|---|---|---|---|---|
| 01 | icdar-2013 | Computer Vision · Scene Text Detection | h-mean | 95.2% | #1 | 2019-04-03 | source ↗ |
| 02 | icdar-2013 | Computer Vision · Scene Text Detection | precision | 97.4% | #1 | 2019-04-03 | source ↗ |
| 03 | icdar-2013 | Computer Vision · Scene Text Detection | recall | 93.1% | #1 | 2019-04-03 | source ↗ |
| 04 | icdar-2017-mlt | Computer Vision · Scene Text Detection | h-mean | 73.9% | #1 | 2019-04-03 | source ↗ |
| 05 | CTW1500 | Computer Vision · Scene Text Detection | f-measure | 83.5% | #5 | 2019-04-03 | source ↗ |
| 06 | CTW1500 | Computer Vision · Scene Text Detection | precision | 86.0% | #5 | 2019-04-03 | source ↗ |
| 07 | CTW1500 | Computer Vision · Scene Text Detection | recall | 81.1% | #5 | 2019-04-03 | source ↗ |
| 08 | icdar-2017-mlt | Computer Vision · Scene Text Detection | precision | 80.6% | #10 | 2019-04-03 | source ↗ |
| 09 | icdar-2017-mlt | Computer Vision · Scene Text Detection | recall | 68.2% | #12 | 2019-04-03 | source ↗ |
| 10 | scut-ctw1500 | Computer Vision · Optical Character Recognition | precision | 86.0% | #12 | 2019-04-03 | source ↗ |
| 11 | scut-ctw1500 | Computer Vision · Optical Character Recognition | recall | 81.1% | #12 | 2019-04-03 | source ↗ |
| 12 | scut-ctw1500 | Computer Vision · Optical Character Recognition | f-measure | 83.5% | #12 | 2019-04-03 | source ↗ |
| 13 | msra-td500 | Computer Vision · Scene Text Detection | precision | 88.2% | #14 | 2019-04-03 | source ↗ |
| 14 | msra-td500 | Computer Vision · Scene Text Detection | f-measure | 82.9% | #16 | 2019-04-03 | source ↗ |
| 15 | msra-td500 | Computer Vision · Scene Text Detection | recall | 78.2% | #16 | 2019-04-03 | source ↗ |
| 16 | ICDAR 2015 | Computer Vision · Scene Text Detection | recall | 84.3% | #18 | 2019-04-03 | source ↗ |
| 17 | ICDAR 2015 | Computer Vision · Scene Text Detection | precision | 89.8% | #18 | 2019-04-03 | source ↗ |
| 18 | ICDAR 2015 | Computer Vision · Scene Text Detection | f-measure | 86.9% | #20 | 2019-04-03 | source ↗ |
| 19 | Total-Text | Computer Vision · Scene Text Detection | precision | 87.6% | #20 | 2019-04-03 | source ↗ |
| 20 | Total-Text | Computer Vision · Scene Text Detection | recall | 79.9% | #21 | 2019-04-03 | source ↗ |
| 21 | Total-Text | Computer Vision · Scene Text Detection | f-measure | 83.6% | #23 | 2019-04-03 | source ↗ |
Rank column shows this model’s position vs all other models scored on the same benchmark + metric (competitors after the slash). #1 in red means current SOTA. Sorted by rank, then newest result.
§ 03 · Papers
2 papers with results for CRAFT.
- 2019-04-03· Computer Vision· 3 results
Character Region Awareness for Text Detection
- 2019-04-03· Computer Vision· 18 results
Character Region Awareness for Text Detection
§ 04 · Related models
Other Unknown models scored on Codesota.
fglihai
Unknown params · 6 results · 1 SOTA
CLIP4STR-L
Unknown params · 1 result · 1 SOTA
USYD NLP_CS29-2
Unknown params · 6 results
Corner-based Region Proposals
Unknown params · 3 results
EAST + VGG16
Unknown params · 3 results
SSTD
Unknown params · 3 results
TextBoxes++_MS
Unknown params · 3 results
WordSup (VGG16-synth-coco)
Unknown params · 3 results
§ 05 · Sources & freshness
Where these numbers come from.
papers-with-code
18
results
arxiv
3
results
21 of 21 rows marked verified.