README.md

metadata

license: apache-2.0
language:
  - en
  - de
tags:
  - object-detection
  - document-understanding
  - invoice
  - layout-analysis
  - bounding-box
  - ocr-prep
task_categories:
  - object-detection
size_categories:
  - n<1K
pretty_name: Invoice Layout Annotated BBox Dataset
dataset_info:
  features:
    - name: image
      dtype: image
    - name: image_id
      dtype: int64
    - name: document_id
      dtype: int64
    - name: document_filename
      dtype: string
    - name: page_number
      dtype: int64
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: objects
      struct:
        - name: id
          list: int64
        - name: area
          list: float64
        - name: bbox
          list:
            list: float32
            length: 4
        - name: category
          list:
            class_label:
              names:
                '0': invoice_metadata
                '1': vendor_block
                '2': customer_block
                '3': table_block
                '4': line_item
                '5': summary_block
                '6': payment_block
                '7': Column
  splits:
    - name: train
      num_bytes: 40359783
      num_examples: 76
  download_size: 36093604
  dataset_size: 40359783
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Manually annotated invoice page images exported from AnnotateEverything, with axis-aligned bounding boxes for 8 document-layout regions. Built for training object detectors (YOLO, DETR, etc.) on invoice macro-structure.

Dataset summary

Property	Value
Pages	76
Documents	1
Source PDF	train_images.pdf
Total annotations	771
Avg boxes / page	10.14
Image width range	425 – 2853 px
Image height range	570 – 4096 px
Export date	2026-06-22T19:27:38.375Z
Annotation tool	AnnotateEverything
Project	Invoices (id=2)

Classes

ID	Name	Description	Color	Count
0	invoice_metadata	Invoice number, date, header metadata	#f59e0b	83
1	vendor_block	Seller / vendor address block	#22c55e	73
2	customer_block	Client / customer address block	#ef4444	74
3	table_block	Line items table region	#ec4899	76
4	line_item	Individual line item row	#8b5cf6	257
5	summary_block	Totals / summary section	#3b82f6	78
6	payment_block	Payment / IBAN block	#06b6d4	55
7	Column	Table column header row	#64748b	75

Dataset Viewer schema

The train split uses COCO-style objects with [x, y, width, height] bboxes (top-left origin):

Column	Type
image	Image
image_id, document_id, page_number, width, height	int
document_filename	string
objects.bbox	list of [x, y, w, h]
objects.category	ClassLabel (8 layout regions)
objects.area, objects.id	float / int

Raw export layout

The original AnnotateEverything export is preserved under raw_export/:

raw_export/
├── manifest.json
├── annotations.json
└── documents/.../pages/page_NNN/image.png

Sample pages

Page 1 (8 boxes)

Original	Annotated

Page 20 (13 boxes)

Original	Annotated

Page 39 (10 boxes)

Original	Annotated

Page 58 (4 boxes)

Original	Annotated

Page 76 (8 boxes)

Original	Annotated

Page 19 (14 boxes)

Original	Annotated

Label distribution

Label	Count
line_item	257
invoice_metadata	83
summary_block	78
table_block	76
Column	75
customer_block (Receiver)	74
vendor_block (Sender)	73
payment_block	55

Usage

Load with Hugging Face Datasets (recommended)

from datasets import load_dataset

ds = load_dataset("AvoCahDoe/invoice-annotated-bbox")
example = ds["train"][0]
print(example["objects"])  # COCO-style bboxes + categories
example["image"].show()

Load raw AnnotateEverything export

import json
from huggingface_hub import hf_hub_download

ann_path = hf_hub_download("AvoCahDoe/invoice-annotated-bbox", "raw_export/annotations.json", repo_type="dataset")
with open(ann_path) as f:
    pages = json.load(f)

Convert to YOLO (invoice-extractor)

python scripts/prepare_annotated_dataset.py

License

Apache 2.0