backbone     mAP (%)     63.4

[Kusetogullari et al, 2021] - <ref>      Dataset I: 250K single digits

Dataset II: 200K multi-digits
Dataset III: 25K bounding boxes
Dataset I: 0 - 9
Handwritten digit

recognition and detection
Swedish      Swedish handwritten
document crops
from 1800 - 1940
Color      $240\times 210$
Pixels     JPG     Images organized in
labeled folders      Folders    DIGITNET-dect [Kusetogullari et al, 2021]     DR (%)   75.96

- <ref>     1K pages
28,255 lines
281,914 characters
85 writers     Writter-id and
150 years of origin
classes    Automatic transcription
Handwriting recognition     Dutch      Early Modern
Manuscripts       Color
Grayscale      150 - 300
dpi    JPG     Transcriptions
Year of origin    UTF8
TXT      CNN/BLSTM/CTC [Voigtlaender et al, 2016, Nina, 2018]     CER (%)     $11.9 \pm 0.68$

20 train images
100 test images    Text
Text line
Page segmentation
Text line detection
Arabic     Arabic scientific
manuscripts from
9-19 CE      Color     N/A     TIFF
Page-level: polygon text
regions, paragraphs, graphics/
line drawing, and text lines
Transcriptions      PAGE XML    Page: Google
Text-line: RDI OCR
OCR: RDI OCR
Success rate (%)
Success rate (%)
Flex ca (%)     69.30

ICDAR 2019
[Christlein et al, 2019] - <ref>
Train and validation
from <ref>
20K test images

10K writers
Writers from manuscript
books, letter, or charters
Image retrieval
based on writer
N/A     Handwritten document
pages of manuscript books,
letters, charters and legal docs
from  various institutions
Color     High and low
quality factor
2K pixel
larger dimension

JPG     Writer ID next
to image name
SIFT [LoweDavid, 2004] + Pathlet [Lai and Jin, 2019]
(SCUT submission)     acc (%)
mAP (%)    97.4

HTR Benchmarks
[Sánchez et al, 2019] - <ref>
ICFHR-2014: 433 pages, 11,473 lines
106K running words, 550K characters

ICDAR-2015: 796 pages, 21,752 lines

186K running words, 955K characters

ICFHR-2016: 450 pages, 10K lines

43K running words, 260K characters

ICDAR-2017: 10K pages, 206K lines

1.7M running words, 8M characters
ICFHR-2014: 9K lexicon, 86 character set

ICDAR-2015: 17K lexicon, 87 character set

ICFHR-2016: 8K lexicon, 92 character set

ICDAR-2017: 4K lexicon, 104 character set
Handwritten text
Early modern German
Handwritten document pages
from the Bentham and
the Ratsprotokolle
(1470-1805) collections
Color     300 dpi or
75-300 dpi
Transcriptions and
coordinates at line-level      PAGE XML
CRNN + N-gram LM    CER (%)
WER (%)       CER   WER

ICFHR-2014     5.0     9.7

ICDAR-2015     12.8     30.0

ICFHR-2016     4.5     17.5

ICDAR-2017    5.8     17.6

- <ref>      2,271 Images

25K Elements
Page frame

Title & Text region
Title, Subtitle
Layout analysis

Element extraction
Japanese      Biography scans      Color     N/A     JPG
Page frame
Row region
Text block boxes
Reading order
Hierarchical dependencies    JSON
(COCO)     Faster-RCNN [Ren et al, 2015]
Mask-RCNN [He et al, 2020]
Retinanet[Lin et al, 2017]     mAP (%)     81.991

ICFHR 2020
[Seuret et al, 2020] - <ref>
220K fragment images
(101,706 train - 20,019 test)
9,800K writers
(8,717 train - 1,152 test)    Image retrieval
per writer and
per image
Manuscript pages
from books of the
European Middle Ages
9 - 15 century CE
High and low
quality factor
2K pixel
larger dimension

Writer ID, Page ID,
and Fragment ID
on image name
Image name

ResNet50 [He et al, 2016] + $\chi^2$ distance
mAP (%)
acc (%)
Pr@10 (%)
Pr@100 (%)
Writer     Image
33.5     22.6
77.1   36.4
53.1   31.2
50.4   58.9

width=3cm, height=29.7cm, left=8.2cm

Historical document image datasets with information about statistics, classes, tasks, language, document type, input visual aspects, ground truth, and benchmarks present in their original papers or competitions. The datasets are presented in the same order as in Table <ref> (earliest to latest).

6cGeneral Information

Dataset name
[reference] - section   Statistics   Classes   Task   Language    Document type
Format    Annotation     Format     Model     Metric     Performance

ICDAR 2021
[Anna et al, 2021] - <ref>
Font: 35k train, 5,506 test images

Script: CLaMM 2017 train, 1,256 test images

Date: 11,294 train, 2,516 test images

Location: 5,517 train, 60
val, 300 test images
Font: same as Section <ref>
without "other font" and
"not a font" classes (10 classes)

Script: same as Section <ref>

Date: date ranges
Location: Cluny, Corbie, Citeaux, Florence,
Fonteney, Himanis, Milan, Paris, Signy
MontSaintMichel, SaintBertin
SaintGermainDesPres, SainMatrialDeLinoges
date, and location
Handwritten and

printed page
images in Latin

Label next
to image
Font: ResNeXt-50 [Xie et al, 2017] on multi-scales

Script: ResNeXt-50 [Xie et al, 2017] + linear interpolation

Date: CNN on text lines

Location: ResNeXt-50 [Xie et al, 2017] on multi-scales

Overall acc (%)
Overall acc (%)
MAE (%)

Overall acc (%)

BIR Database
[Anna et al, 2021] - <ref>
35 documents
285 pages
2,106 bold words
5,745 italic words
80,168 regular words
880,169 total words
Regular     Style classification
Word detection
Printed pages
from sale catalogues
and exhibitions
from the 19
and 20 centuries
Color     N/A
JPG        Word bounding
boxes and
word font class

Word detection: YOLOv5m [Jocher et al, 2021]
Style classification: MobileNetV2 [Sandler et al, 2018]
(TVT)       0.91

[Ziomek and Middleton, 2021] - <ref>
500 images

Table body
Table structure
N/A      Scanned printed
and mixed pages
from measurement
logbooks from
1700 - modern days
Color     N/A
JPG        Full table, individual
cells and coarse
segmentation cell
bounding boxes

CascadeTabNet [Prasad et al, 2020]
CascadeTabNet [Prasad et al, 2020] + Postprocessing [Schubert et al, 2017]

WA F1-score     +cTDaR19     Ind. cells     Coarse cells
0.071     0.047    0.385
0.170     0.263     0.578

Digital Peter
[Mark et al, 2021] - <ref>
9,694 text-line images

(6,237 train, 1,930
validation, 1,527 test)

265,788 characters

50,998 words

N/A      Line segmentation
Line recognition
Manuscripts from
Peter the Great
written from
Color     N/A
JPG    Line polygons
Line transcriptions    COCO
CNN-GRU-CTC [Graves et al, 2006]
CER (%)
WER (%)
String acc (%)

[Mark et al, 2021] - <ref>
202 images
11,285 lines
74,675 words

Script classes: Ashkenazi,
Italian, Sephardi      Line segmentation
Text transcription

written from
JPG    Baselines
Transcriptions    XML
and recognition
models based on
kraken OCR
acc (%)
WER (%)

97.24 (All texts)

[Romero and Sánchez, 2021] - <ref>
208 pages, 33,739 lines
66,814 running words,
15,471 relevant information

1,483 lexicon, 76 characters     Text recognition
Layout analysis
Information extraction
Pages from a ship
weather log book
that sailed from

N/A     Region blocks, columns,
rows, lines
Relevant information    N/A
Layout: NN-based [Quirós, 2018]
OCR: CRNN + CTC + LM

Information extraction based on [Lang et al, 2018]
P - R - F1

CER (%) - WER (%)
P - R - F1

0.91 - 0.72 - 0.80

2.7 - 4.4
cell pos: 0.95 - 0.95 - 0.95

line geom: 0.79 - 0.79 - 0.785

[Maarand et al, 2022] - <ref>
828 pages, 23,732 lines
164,922 words
752,080 characters

12 writers      Handwritten
text recognition
Pages from private
correspondences and
diaries from 12 writers
written between 1820-1950
N/A      Transcriptions    PAGE XML
PyLaia expert [Puigcerver and Mocholí, 2018]
Kaldi expert [Arora et al, 2019]
CER (%)
WER (%)

[Constum et al, 2022] - <ref>
Generic: 128 train, 16 val, 16 test pages
3,840 train, 480 val, 480 test lines

Belleville: 38 train, 5 val, 6 test pages
1,140 train, 150 val, 180 test lines

Chaussée d'Antin: 625 train, 78 val, 77 test lines

Generic - Belleville - Chaussée d'Antin
80 - 1 - 10 writers     Handwritten
table recognition
and information extraction
Pages from Paris
census tables
from 1926
200 dpi
TIFF      Line bbox coordinates
Transcriptions     XML
Optical model [Coquenet et al, 2022]
+ self-training
with extracted corpus
CER (%)
WER (%)

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