Datasets:
File size: 1,147 Bytes
356fae1 fdcb5c5 ab78c9d 493cdf5 2148280 ab78c9d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
---
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
tags:
- invoices
- data extraction
- invoice
- FATURA2
pretty_name: FATURA 2 invoices
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} } |