---
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} }