Datasets:
File size: 1,558 Bytes
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---
dataset_info:
features:
- name: image
dtype: image
- name: ner_tags
sequence: int64
- name: words
sequence: string
- name: bboxes
sequence:
sequence: int64
splits:
- name: train
num_bytes: 477503369
num_examples: 10000
download_size: 342662174
dataset_size: 477503369
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- text-classification
- token-classification
language:
- en
tags:
- invoices
- invoice
- FATURA
pretty_name: FATURA2 invoices dataset
size_categories:
- 1K<n<10K
---
The dataset consists of 10000 jpg images with white backgrounds, 10000 jpg images with colored backgrounds (the same colors used in the paper) as well as 3x10000 json annotation files. The images are generated from 50 different templates.
https://zenodo.org/records/10371464
---
dataset_info:
features:
- name: image
dtype: image
- name: ner_tags
sequence: int64
- name: words
sequence: string
- name: bboxes
sequence:
sequence: int64
splits:
- name: train
num_bytes: 477503369.0
num_examples: 10000
download_size: 342662174
dataset_size: 477503369.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
@misc{limam2023fatura, title={FATURA: A Multi-Layout Invoice Image Dataset for Document Analysis and Understanding}, author={Mahmoud Limam and Marwa Dhiaf and Yousri Kessentini}, year={2023}, eprint={2311.11856}, archivePrefix={arXiv}, primaryClass={cs.CV} } |