task_path
stringlengths 3
199
⌀ | dataset
stringlengths 1
128
⌀ | model_name
stringlengths 1
223
⌀ | paper_url
stringlengths 21
601
⌀ | metric_name
stringlengths 1
50
⌀ | metric_value
stringlengths 1
9.22k
⌀ |
|---|---|---|---|---|---|
Optical Character Recognition (OCR)
|
SUT
|
Tesseract
|
https://ieeexplore.ieee.org/document/10326243
|
Character Error Rate (CER)
|
0.083
|
Optical Character Recognition (OCR)
|
SUT
|
EasyOCR
|
https://ieeexplore.ieee.org/document/10326243
|
Character Error Rate (CER)
|
0.072
|
Optical Character Recognition (OCR)
|
im2latex-100k
|
I2L-STRIPS
|
http://arxiv.org/abs/1802.05415v2
|
BLEU
|
88.86%
|
Optical Character Recognition (OCR)
|
FSNS - Test
|
AttentionOCR_Inception-resnet-v2_Location
|
http://arxiv.org/abs/1704.03549v4
|
Sequence error
|
15.8
|
Optical Character Recognition (OCR)
|
FSNS - Test
|
SEE
|
https://arxiv.org/abs/1712.05404
|
Sequence error
|
22
|
Optical Character Recognition (OCR)
|
FSNS - Test
|
STREET
|
http://arxiv.org/abs/1702.03970v1
|
Sequence error
|
27.54
|
Optical Character Recognition (OCR)
|
I2L-140K
|
I2L-NOPOOL
|
http://arxiv.org/abs/1802.05415v2
|
BLEU
|
89.09%
|
Optical Character Recognition (OCR)
|
I2L-140K
|
I2L-STRIPS
|
http://arxiv.org/abs/1802.05415v2
|
BLEU
|
89%
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
GPT-4o
|
https://arxiv.org/abs/2502.06445v1
|
Character Error Rate (CER)
|
0.2378
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
GPT-4o
|
https://arxiv.org/abs/2502.06445v1
|
Word Error Rate (WER)
|
0.5117
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
GPT-4o
|
https://arxiv.org/abs/2502.06445v1
|
Average Accuracy
|
76.22
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2502.06445v1
|
Character Error Rate (CER)
|
0.2387
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2502.06445v1
|
Word Error Rate (WER)
|
0.2385
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2502.06445v1
|
Average Accuracy
|
76.13
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
Claude-3 Sonnet
|
https://arxiv.org/abs/2502.06445v1
|
Character Error Rate (CER)
|
0.3229
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
Claude-3 Sonnet
|
https://arxiv.org/abs/2502.06445v1
|
Word Error Rate (WER)
|
0.4663
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
Claude-3 Sonnet
|
https://arxiv.org/abs/2502.06445v1
|
Average Accuracy
|
67.71
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
RapidOCR
|
https://arxiv.org/abs/2502.06445v1
|
Character Error Rate (CER)
|
0.7620
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
RapidOCR
|
https://arxiv.org/abs/2502.06445v1
|
Word Error Rate (WER)
|
0.4302
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
RapidOCR
|
https://arxiv.org/abs/2502.06445v1
|
Average Accuracy
|
56.98
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
EasyOCR
|
https://arxiv.org/abs/2502.06445v1
|
Character Error Rate (CER)
|
0.5070
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
EasyOCR
|
https://arxiv.org/abs/2502.06445v1
|
Word Error Rate (WER)
|
0.8262
|
Optical Character Recognition (OCR)
|
VideoDB's OCR Benchmark Public Collection
|
EasyOCR
|
https://arxiv.org/abs/2502.06445v1
|
Average Accuracy
|
49.30
|
Optical Character Recognition (OCR)
|
Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
|
DTrOCR
|
https://arxiv.org/abs/2308.15996v1
|
Accuracy (%)
|
89.6
|
Optical Character Recognition (OCR)
|
Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
|
DTrOCR 105M
|
https://arxiv.org/abs/2308.15996v1
|
Accuracy (%)
|
89.6
|
Optical Character Recognition (OCR)
|
Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
|
MaskOCR-L
|
https://arxiv.org/abs/2206.00311v3
|
Accuracy (%)
|
82.6
|
Optical Character Recognition (OCR)
|
Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
|
TransOCR
|
http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper.html
|
Accuracy (%)
|
72.8
|
Optical Character Recognition (OCR)
|
Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
|
SRN
|
https://arxiv.org/abs/2003.12294v1
|
Accuracy (%)
|
65.0
|
Optical Character Recognition (OCR)
|
Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
|
MORAN
|
http://arxiv.org/abs/1901.03003v1
|
Accuracy (%)
|
64.3
|
Optical Character Recognition (OCR)
|
Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
|
SEED
|
https://arxiv.org/abs/2005.10977v1
|
Accuracy (%)
|
61.2
|
Optical Character Recognition (OCR) > Active Learning
|
CIFAR10 (10,000)
|
TypiClust
|
https://arxiv.org/abs/2202.02794v4
|
Accuracy
|
93.2
|
Optical Character Recognition (OCR) > Active Learning
|
CIFAR10 (10,000)
|
PT4AL
|
https://arxiv.org/abs/2201.07459v3
|
Accuracy
|
93.1
|
Optical Character Recognition (OCR) > Active Learning
|
CIFAR10 (10,000)
|
Learning loss
|
https://arxiv.org/abs/1905.03677v1
|
Accuracy
|
91.01
|
Optical Character Recognition (OCR) > Active Learning
|
CIFAR10 (10,000)
|
CoreGCN
|
https://arxiv.org/abs/2006.10219v3
|
Accuracy
|
90.70
|
Optical Character Recognition (OCR) > Active Learning
|
CIFAR10 (10,000)
|
Core-set
|
http://arxiv.org/abs/1708.00489v4
|
Accuracy
|
89.92
|
Optical Character Recognition (OCR) > Active Learning
|
CIFAR10 (10,000)
|
Random Baseline (Resnet18)
|
https://arxiv.org/abs/2002.09564v3
|
Accuracy
|
88.45
|
Optical Character Recognition (OCR) > Active Learning
|
CIFAR10 (10,000)
|
Random Baseline (VGG16)
|
https://arxiv.org/abs/2002.09564v3
|
Accuracy
|
85.09
|
Optical Character Recognition (OCR) > Active Learning > Active Object Detection
|
PASCAL VOC 07+12
|
RetinaNet
|
https://arxiv.org/abs/2104.02324v1
|
mAP
|
(47.18, 58.41, 64.02, 67.72, 69.79, 71.07, 72.27) on 5% ~ 20%
|
Optical Character Recognition (OCR) > Active Learning > Active Object Detection
|
PASCAL VOC 07+12
|
SSD
|
https://arxiv.org/abs/2104.02324v1
|
mAP
|
(53.62, 62.86, 66.83, 69.33, 70.80, 72.21, 72.84, 73.74, 74.18, 74.91) on 1k ~ 10k
|
Optical Character Recognition (OCR) > Active Learning > Active Object Detection
|
COCO (Common Objects in Context)
|
RetinaNet
|
https://arxiv.org/abs/2104.02324v1
|
AP
|
(7.3, 13.8, 16.9, 19.1, 20.8) on 2% ~ 10%
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Saint Gall
|
StackMix+Blots
|
https://arxiv.org/abs/2108.11667v1
|
CER
|
3.65
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ 2016
|
DAN
|
https://arxiv.org/abs/2203.12273v4
|
CER (%)
|
3.22
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ 2016
|
DAN
|
https://arxiv.org/abs/2203.12273v4
|
WER (%)
|
13.63
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ 2016
|
HTR-VT(line-level)
|
https://arxiv.org/abs/2409.08573v1
|
CER (%)
|
3.9
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ 2016
|
HTR-VT(line-level)
|
https://arxiv.org/abs/2409.08573v1
|
WER (%)
|
16.5
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Bentham
|
StackMix+Blots
|
https://arxiv.org/abs/2108.11667v1
|
CER
|
1.73
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM(line-level)
|
TrOCR
|
https://arxiv.org/abs/2109.10282v5
|
Test CER
|
3.4
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM(line-level)
|
TrOCR
|
https://arxiv.org/abs/2109.10282v5
|
Test WER
|
-
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM(line-level)
|
HTR-VT
|
https://arxiv.org/abs/2409.08573v1
|
Test CER
|
4.7
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM(line-level)
|
HTR-VT
|
https://arxiv.org/abs/2409.08573v1
|
Test WER
|
14.9
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM(line-level)
|
VAN
|
https://arxiv.org/abs/2012.03868v2
|
Test CER
|
5.0
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM(line-level)
|
VAN
|
https://arxiv.org/abs/2012.03868v2
|
Test WER
|
16.3
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM(line-level)
|
OrigamiNet-12
|
https://arxiv.org/abs/2006.07491v1
|
Test CER
|
6.0
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM(line-level)
|
OrigamiNet-12
|
https://arxiv.org/abs/2006.07491v1
|
Test WER
|
22.3
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM(line-level)
|
GFCN
|
https://arxiv.org/abs/2012.04961v1
|
Test CER
|
8.0
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM(line-level)
|
GFCN
|
https://arxiv.org/abs/2012.04961v1
|
Test WER
|
28.6
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
DTrOCR 105M
|
https://arxiv.org/abs/2308.15996v1
|
CER
|
2.38
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Self-Attention + CTC + language model
|
https://arxiv.org/abs/2104.07787v2
|
CER
|
2.75
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
TrOCR-large 558M
|
https://arxiv.org/abs/2109.10282v5
|
CER
|
2.89
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Transformer + CNN
|
https://arxiv.org/abs/2104.07787v2
|
CER
|
2.96
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
TrOCR-base 334M
|
https://arxiv.org/abs/2109.10282v5
|
CER
|
3.42
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
TrOCR-small 62M
|
https://arxiv.org/abs/2109.10282v5
|
CER
|
4.22
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
VAN
|
https://arxiv.org/abs/2012.03868v2
|
CER
|
4.32
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
VAN
|
https://arxiv.org/abs/2012.03868v2
|
WER
|
16.24
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Transformer w/ CNN (+synth)
|
https://arxiv.org/abs/2005.13044v1
|
CER
|
4.67
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
HTR-VT(line-level)
|
https://arxiv.org/abs/2409.08573v1
|
CER
|
4.7
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
HTR-VT(line-level)
|
https://arxiv.org/abs/2409.08573v1
|
WER
|
14.9
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Easter2.0
|
https://arxiv.org/abs/2205.14879v1
|
CER
|
6.21
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
FPHR+Aug Paragraph Level (~145 dpi)
|
https://arxiv.org/abs/2103.06450v3
|
CER
|
6.3
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Decouple Attention Network
|
https://arxiv.org/abs/1912.10205v1
|
CER
|
6.4
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Decouple Attention Network
|
https://arxiv.org/abs/1912.10205v1
|
WER
|
19.6
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Start, Follow, Read
|
http://openaccess.thecvf.com/content_ECCV_2018/html/Curtis_Wigington_Start_Follow_Read_ECCV_2018_paper.html
|
CER
|
6.4
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Start, Follow, Read
|
http://openaccess.thecvf.com/content_ECCV_2018/html/Curtis_Wigington_Start_Follow_Read_ECCV_2018_paper.html
|
WER
|
23.2
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
FPHR+Aug Line Level (~145 dpi)
|
https://arxiv.org/abs/2103.06450v3
|
CER
|
6.5
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Leaky LP Cell
|
http://arxiv.org/abs/1902.11208v1
|
CER
|
6.6
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Leaky LP Cell
|
http://arxiv.org/abs/1902.11208v1
|
WER
|
15.9
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
FPHR Paragraph Level (~145 dpi)
|
https://arxiv.org/abs/2103.06450v3
|
CER
|
6.7
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
IAM
|
Transformer w/ CNN
|
https://arxiv.org/abs/2005.13044v1
|
CER
|
7.62
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
SIMARA
|
DAN
|
https://arxiv.org/abs/2304.13606v1
|
CER (%)
|
6.46
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
SIMARA
|
DAN
|
https://arxiv.org/abs/2304.13606v1
|
WER (%)
|
14.79
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ2016(line-level)
|
HTR-VT
|
https://arxiv.org/abs/2409.08573v1
|
Test CER
|
3.9
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ2016(line-level)
|
HTR-VT
|
https://arxiv.org/abs/2409.08573v1
|
Test WER
|
16.5
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ2016(line-level)
|
VAN
|
https://arxiv.org/abs/2012.03868v2
|
Test CER
|
4.1
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ2016(line-level)
|
VAN
|
https://arxiv.org/abs/2012.03868v2
|
Test WER
|
16.3
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ2016(line-level)
|
DAN
|
https://arxiv.org/abs/2203.12273v4
|
Test CER
|
4.1
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ2016(line-level)
|
DAN
|
https://arxiv.org/abs/2203.12273v4
|
Test WER
|
17.6
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ2016(line-level)
|
Span
|
https://arxiv.org/abs/2102.08742v1
|
Test CER
|
4.6
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ2016(line-level)
|
Span
|
https://arxiv.org/abs/2102.08742v1
|
Test WER
|
21.1
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ2016(line-level)
|
CNN + BLSTM
|
https://arxiv.org/abs/1903.07377v2
|
Test CER
|
4.7
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
READ2016(line-level)
|
CNN + BLSTM
|
https://arxiv.org/abs/1903.07377v2
|
Test WER
|
-
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Belfort
|
PyLaia (all transcriptions + agreement-based split)
|
https://arxiv.org/abs/2306.10878v1
|
CER (%)
|
4.34
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Belfort
|
PyLaia (all transcriptions + agreement-based split)
|
https://arxiv.org/abs/2306.10878v1
|
WER (%)
|
15.14
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Belfort
|
PyLaia (rover consensus + agreement-based split)
|
https://arxiv.org/abs/2306.10878v1
|
CER (%)
|
4.95
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Belfort
|
PyLaia (rover consensus + agreement-based split)
|
https://arxiv.org/abs/2306.10878v1
|
WER (%)
|
17.08
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Belfort
|
PyLaia (human transcriptions + agreement-based split)
|
https://arxiv.org/abs/2306.10878v1
|
CER (%)
|
5.57
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Belfort
|
PyLaia (human transcriptions + agreement-based split)
|
https://arxiv.org/abs/2306.10878v1
|
WER (%)
|
19.12
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Belfort
|
PyLaia (human transcriptions + random split)
|
https://arxiv.org/abs/2306.10878v1
|
CER (%)
|
10.54
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Belfort
|
PyLaia (human transcriptions + random split)
|
https://arxiv.org/abs/2306.10878v1
|
WER (%)
|
28.11
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
HKR
|
StackMix+Blots
|
https://arxiv.org/abs/2108.11667v1
|
CER
|
3.49
|
Optical Character Recognition (OCR) > Handwritten Text Recognition
|
Digital Peter
|
StackMix+Blots
|
https://arxiv.org/abs/2108.11667v1
|
CER
|
2.5
|
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