Datasets:
File size: 1,692 Bytes
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---
language:
- en
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- feature-extraction
pretty_name: FATURA 2 invoices
tags:
- invoices
- data extraction
- invoice
- FATURA2
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: ner_tags
sequence: int64
- name: bboxes
sequence:
sequence: int64
- name: tokens
sequence: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 411874484.6
num_examples: 8600
- name: test
num_bytes: 60569760.6
num_examples: 1400
download_size: 342750666
dataset_size: 472444245.20000005
---
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} } |