Datasets:
metadata
task_categories:
- image-to-text
pretty_name: ATR benchmark
size_categories:
- n<1K
language:
- fr
- la
- en
- 'no'
- ar
- zh
- de
- nl
tags:
- atr
- htr
- ocr
dataset_info:
features:
- name: dataset
dtype: string
- name: image
dtype: image
- name: text
dtype: string
- name: level
dtype: string
splits:
- name: test
num_bytes: 180132193
num_examples: 133
download_size: 178873479
dataset_size: 180132193
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
ATR benchmark - Page/paragraph level
Dataset Description
- Homepage: ATR benchmark
- Point of Contact: TEKLIA
Dataset Summary
The ATR benchmark dataset is a multilingual dataset that includes 463 document images, at page or paragraph level. This dataset has been designed to test ATR models and combines data from several public datasets:
- BnL Historical Newspapers
- CASIA-HWDB2
- Churro
- DIY History - Social Justice
- DAI-CRETDHI
- Esposalles
- FINLAM - Historical Newspapers
- Horae - Books of hours
- IAM
- NDLOCR
- NorHand v3
- OpenITI
- Marius PELLET
- QARI
- RASM
- READ-2016
- RIMES
- ScribbleLens
Images are in their original size.
Split
| dataset | images | language |
|---|---|---|
| BnL Historical Newspapers | 3 | German |
| CASIA-HWDB2 | 10 | Chinese |
| Churro | 290 | Multi-lingual |
| DAI-CRETDHI | 10 | French |
| DIY History - Social Justice | 20 | English |
| Esposalles | 10 | Catalan |
| FINLAM - Historical Newspapers | 10 | English / French |
| Horae - Books of hours | 10 | Latin |
| IAM | 10 | English |
| NDLOCR | 10 | Japanese |
| NorHand v3 | 10 | Norwegian |
| OpenITI | 10 | Arabic |
| Marius PELLET | 10 | French |
| QARI | 10 | Arabic |
| RASM | 10 | Arabic |
| READ-2016 | 10 | German |
| RIMES | 10 | French |
| ScribbleLens | 10 | Dutch |
Dataset Structure
Data Instances
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size={img size} at 0x1A800E8E190,
'text': '{transcription}'
}
Data Fields
image: a PIL.Image.Image object containing the image. Note that when accessing the image column (using dataset[0]["image"]), the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].dataset: the name of the original dataset.text: the label transcription of the image.level: full document page or a single paragraph.