Optical Character Recognition
Extracting text from document images
Optical Character Recognition is a key task in computer vision. Below you will find the standard benchmarks used to evaluate models, along with current state-of-the-art results.
Benchmarks & SOTA
cnn-/-daily-mail
Dataset from Papers With Code
State of the Art
Scrambled code + broken (alter)
48.18
rouge-1
scut-ctw1500
Dataset from Papers With Code
State of the Art
FAST-T-512
129.1
fps
icdar2013
Dataset from Papers With Code
State of the Art
DTrOCR 105M
99.4
accuracy
dart
Dataset from Papers With Code
State of the Art
FactT5B
97.6
factspotter
icdar2015
Dataset from Papers With Code
State of the Art
DTrOCR 105M
93.5
accuracy
tabfact
Dataset from Papers With Code
State of the Art
ARTEMIS-DA
93.1
test
sun-rgb-d
Dataset from Papers With Code
State of the Art
IM3D
64.4
iou
inverse-text
Dataset from Papers With Code
State of the Art
DeepSolo (ViTAEv2-S, TextOCR)
75.8
f-measure-full-lexicon
pendigits
Dataset from Papers With Code
State of the Art
DnC-SC
82.86
nmi
videodb's-ocr-benchmark-public-collection
Dataset from Papers With Code
State of the Art
GPT-4o
OpenAI
76.22
accuracy
lam(line-level)
Dataset from Papers With Code
State of the Art
GFCN
18.5
test-wer
howsumm-step
Dataset from Papers With Code
State of the Art
LexRank (query: step title)
39.6
rouge-1
e2e
Dataset from Papers With Code
State of the Art
HTLM (fine-tuning)
70.8
rouge-l
urdudoc
Dataset from Papers With Code
State of the Art
ContourNet [69]
88.68
recall
howsumm-method
Dataset from Papers With Code
State of the Art
LexRank (query: method + article + steps titles)
53.5
rouge-1
iam(line-level)
Dataset from Papers With Code
State of the Art
GFCN
28.6
test-wer
read2016(line-level)
Dataset from Papers With Code
State of the Art
Span
21.1
test-wer
KITAB-Bench
KITAB Arabic OCR Benchmark
8,809 Arabic text samples across 9 domains. Tests Arabic script recognition.
State of the Art
PaddleOCR
Baidu
0.790
cer
belfort
Dataset from Papers With Code
State of the Art
PyLaia (human transcriptions + random split)
28.11
wer
wikibio
Dataset from Papers With Code
State of the Art
MBD
56.16
parent
codesearchnet---java
Dataset from Papers With Code
State of the Art
CodeTrans-MT-Large
21.87
smoothed-bleu-4
codesearchnet---javascript
Dataset from Papers With Code
State of the Art
Transformer
25.61
smoothed-bleu-4
codesearchnet---php
Dataset from Papers With Code
State of the Art
CodeTrans-MT-Base
26.23
smoothed-bleu-4
reuters-21578
Dataset from Papers With Code
State of the Art
ApproxRepSet
97.17
accuracy
codesearchnet---ruby
Dataset from Papers With Code
State of the Art
CodeTrans-MT-Base
15.26
smoothed-bleu-4
codesearchnet---go
Dataset from Papers With Code
State of the Art
CodeBERT (MLM)
26.79
smoothed-bleu-4
codesearchnet
Dataset from Papers With Code
State of the Art
CodeBERT (MLM+RTD)
15.99
smoothed-bleu-4
benchmarking-chinese-text-recognition:-datasets,-b
Dataset from Papers With Code
State of the Art
DTrOCR
89.6
accuracy
codesearchnet---python
Dataset from Papers With Code
State of the Art
CodeTrans-MT-Base
20.39
smoothed-bleu-4
mldoc-zero-shot-english-to-french
Dataset from Papers With Code
State of the Art
XLMft UDA
96.05
accuracy
webnlg-(unseen)
Dataset from Papers With Code
State of the Art
HTLM (fine-tuning)
48.4
bleu
hoc
Dataset from Papers With Code
State of the Art
BioLinkBERT (large)
88.1
f1
webnlg-(seen)
Dataset from Papers With Code
State of the Art
HTLM (fine-tuning)
65.4
bleu
webnlg-(all)
Dataset from Papers With Code
State of the Art
HTLM (fine-tuning)
55.6
bleu
mldoc-zero-shot-english-to-spanish
Dataset from Papers With Code
State of the Art
XLMft UDA
96.8
accuracy
tobacco-small-3482
Dataset from Papers With Code
State of the Art
Optimized Text CNN
84
accuracy
mldoc-zero-shot-english-to-russian
Dataset from Papers With Code
State of the Art
XLMft UDA
89.7
accuracy
wikipedia-person-and-animal-dataset
Dataset from Papers With Code
State of the Art
VTM
45.36
rouge
mldoc-zero-shot-english-to-german
Dataset from Papers With Code
State of the Art
XLMft UDA
96.95
accuracy
ThaiOCRBench
Thai OCR Benchmark
2,808 Thai text samples across 13 tasks. Tests Thai script structural understanding.
State of the Art
Claude Sonnet 4
Anthropic
0.840
ted-score
mldoc-zero-shot-english-to-chinese
Dataset from Papers With Code
State of the Art
XLMft UDA
93.32
accuracy
stdw
Dataset from Papers With Code
State of the Art
RetinaNet
0.780
ap
mldoc-zero-shot-english-to-italian
Dataset from Papers With Code
State of the Art
MultiFiT, pseudo
76.02
accuracy
bbcsport
Dataset from Papers With Code
State of the Art
MPAD-path
99.59
accuracy
read-2016
Dataset from Papers With Code
State of the Art
HTR-VT(line-level)
16.5
wer
sut
Dataset from Papers With Code
State of the Art
CNN
86
accuracy
Dataset from Papers With Code
State of the Art
ApproxRepSet
72.6
accuracy
cub-200-2011
Dataset from Papers With Code
State of the Art
Q-SENN
85.9
top-1-accuracy
amazon
Dataset from Papers With Code
State of the Art
ApproxRepSet
94.31
accuracy
rotowire
Dataset from Papers With Code
State of the Art
HierarchicalEncoder + NR + IR
55.88
content-selection-f1
reuters-rcv1/rcv2-german-to-english
Dataset from Papers With Code
State of the Art
Biinclusion (Euro500kReuters)
84.4
accuracy
reuters-rcv1/rcv2-english-to-german
Dataset from Papers With Code
State of the Art
Biinclusion (Euro500kReuters)
92.7
accuracy
fsns---test
Dataset from Papers With Code
State of the Art
STREET
27.54
sequence-error
mldoc-zero-shot-english-to-japanese
Dataset from Papers With Code
State of the Art
MultiFiT, pseudo
69.57
accuracy
dareczech
Dataset from Papers With Code
State of the Art
Query-doc RobeCzech (Roberta-base)
46.73
p-10
bbc-xsum
Dataset from Papers With Code
State of the Art
BigBird-Pegasus
47.12
rouge-1
scidocs-(mesh)
Dataset from Papers With Code
State of the Art
SciNCL
88.7
f1-micro
cedar-signature
Dataset from Papers With Code
State of the Art
Siamese_MultiHeadCrossAttention_SoftAttention (Siamese_MHCA_SA)
5.7
far
classic
Dataset from Papers With Code
State of the Art
REL-RWMD k-NN
96.85
accuracy
clueweb09-b
Dataset from Papers With Code
State of the Art
XLNet
31.1
ndcg-20
dise-2021-dataset
Dataset from Papers With Code
State of the Art
JDeskew
0.860
percentage-correct
i2l-140k
Dataset from Papers With Code
State of the Art
I2L-NOPOOL
89.09
bleu
icdar-2019
Dataset from Papers With Code
State of the Art
DiT-L (Cascade)
96.55
weighted-average-f1-score
imdb-m
Dataset from Papers With Code
State of the Art
Document Classification Using Importance of Sentences
54.8
accuracy
recipe
Dataset from Papers With Code
State of the Art
ApproxRepSet
59.06
accuracy
scidocs-(mag)
Dataset from Papers With Code
State of the Art
SPECTER
82
f1-micro
aapd
Dataset from Papers With Code
State of the Art
KD-LSTMreg
72.9
f1
simara
Dataset from Papers With Code
State of the Art
DAN
14.79
wer
textzoom
Dataset from Papers With Code
State of the Art
CCD-ViT-Small
21.84
average-psnr-db
wos-5736
Dataset from Papers With Code
State of the Art
ConvTextTM
91.28
accuracy
re-docred
Dataset from Papers With Code
State of the Art
VaeDiff-DocRE
0.790
f1
iris
Dataset from Papers With Code
State of the Art
ELSC
97.7
accuracy
mldoc-zero-shot-german-to-french
Dataset from Papers With Code
State of the Art
BiLSTM (Europarl)
75.45
accuracy
mpqa
Dataset from Papers With Code
State of the Art
MPAD-path
89.81
accuracy
jaffe
Dataset from Papers With Code
State of the Art
ELSC
98.6
accuracy
pixraw10p
Dataset from Papers With Code
State of the Art
ELSC
96
accuracy
and-dataset
Dataset from Papers With Code
State of the Art
Siamese_MHCA_SA
0.810
average-f1
im2latex-100k
Dataset from Papers With Code
State of the Art
I2L-STRIPS
88.86
bleu
reuters-de-en
Dataset from Papers With Code
State of the Art
BilBOWA
75
accuracy
reuters-en-de
Dataset from Papers With Code
State of the Art
BilBOWA
86.5
accuracy
iam-d
Dataset from Papers With Code
State of the Art
StackMix+Blots
3.01
cer
iam-b
Dataset from Papers With Code
State of the Art
StackMix+Blots
3.77
cer
hyperpartisan-news-detection
Dataset from Papers With Code
State of the Art
ChuLo
95.38
accuracy
saint-gall
Dataset from Papers With Code
State of the Art
StackMix+Blots
3.65
cer
scene-text-recognition-benchmarks
Dataset from Papers With Code
State of the Art
CCD-ViT-Small
84.9
accuracy
wine
Dataset from Papers With Code
State of the Art
ELSC
75.8
accuracy
wos-11967
Dataset from Papers With Code
State of the Art
HDLTex
86.07
accuracy
hkr
Dataset from Papers With Code
State of the Art
StackMix+Blots
3.49
cer
wos-46985
Dataset from Papers With Code
State of the Art
HDLTex
76.58
accuracy
food-101
Dataset from Papers With Code
State of the Art
Bert
84.41
accuracy
ephoie
Dataset from Papers With Code
State of the Art
LayoutLMv3
99.21
average-f1
dwie
Dataset from Papers With Code
State of the Art
VaeDiff-DocRE
0.731
f1
docred-ie
Dataset from Papers With Code
State of the Art
REXEL
60.1
relation-f1
textseg
Dataset from Papers With Code
State of the Art
CCD-ViT-Small
84.8
iou
yelp-14
Dataset from Papers With Code
State of the Art
KD-LSTMreg
69.4
accuracy
digital-peter
Dataset from Papers With Code
State of the Art
StackMix+Blots
2.5
cer
cl-scisumm
Dataset from Papers With Code
State of the Art
GCN Hybrid
33.88
rouge-2
bentham
Dataset from Papers With Code
State of the Art
StackMix+Blots
1.73
cer
bc8
Dataset from Papers With Code
State of the Art
BioRex+Directionality
56.06
evaluation-macro-f1
warppie10p
Dataset from Papers With Code
State of the Art
ELSC
53.4
accuracy
ba
Dataset from Papers With Code
State of the Art
ELSC
51.8
accuracy
australian
Dataset from Papers With Code
State of the Art
ELSC
70.9
accuracy
arxiv-summarization-dataset
Dataset from Papers With Code
State of the Art
DeepPyramidion
19.99
rouge-2
arxiv-hep-th-citation-graph
Dataset from Papers With Code
State of the Art
DeepPyramidion
47.15
rouge-1
wikilingua-(tr->en)
Dataset from Papers With Code
State of the Art
DOCmT5
31.37
rouge-l
lun
Dataset from Papers With Code
State of the Art
ChuLo
64.4
accuracy
IMPACT-PSNC
IMPACT Polish Digital Libraries Ground Truth
478 pages of ground truth from four Polish digital libraries at 99.95% accuracy. Includes annotations at region, line, word, and glyph levels. Gothic and antiqua fonts.
No results tracked yet
CodeSOTA Polish
CodeSOTA Polish OCR Benchmark
1,000 synthetic and real Polish text images with 5 degradation levels (clean to severe). Tests character-level OCR on diacritics with contamination-resistant synthetic categories. Categories: synth_random (pure character recognition), synth_words (Markov-generated words), real_corpus (Pan Tadeusz, official documents), wikipedia (potential contamination baseline).
No results tracked yet
SROIE
Scanned Receipts OCR and Information Extraction
626 receipt images. Key task: extract company, date, address, total from receipts.
No results tracked yet
PolEval 2021 OCR
PolEval 2021 OCR Post-Correction Task
979 Polish books (69,000 pages) from 1791-1998. Focus on OCR post-correction using NLP methods. Major benchmark for Polish historical document processing.
No results tracked yet
Related Tasks
General OCR Capabilities
Comprehensive benchmarks covering multiple aspects of OCR performance.
Polish OCR
OCR for Polish language including historical documents, gothic fonts, and diacritic recognition.
Image Classification
Categorizing images into predefined classes (ImageNet, CIFAR).
Object Detection
Locating and classifying objects in images (COCO, Pascal VOC).