datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
document-haystack-10pages
Dataset Card for document-haystack-10pages
This is a FiftyOne dataset with 250 samples. It's the 10-page subset of the full dataset.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/document-haystack-10pages")
# Launch the App… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/document-haystack-10pages.icdar2021-historical-document-dating
ICDAR 2021 Historical Document Classification — Task 2 (Dating)
13,810 manuscript page images labelled with the period in which they were produced.
Images come from e-codices, the virtual manuscript library
of Switzerland.
Split
Images
Date range
Median span
Dated to a single year
train
11,294
800–1899
45 years
1,409
test
2,516
800–1921
49 years
264
The label is an interval, not a year
Palaeographers date a manuscript to a range, and the width of… See the full description on the dataset page: https://huggingface.co/datasets/biglam/icdar2021-historical-document-dating.noisy-medical-document-images-ocr
🏥 Noisy Medical Document Images (OCR)
1,000 noisy, synthetic medical document images with structured JSON ground truth — built for Document AI, LayoutLM fine-tuning, and clinical NLP research.
🧭 Overview
This dataset provides 1,000 high-resolution images of two healthcare document types, each degraded with realistic scanning artifacts to simulate real-world OCR conditions:
Category
Count
Description
🧾 Hospital Bills
500
Itemized statements with… See the full description on the dataset page: https://huggingface.co/datasets/hmnshudhmn24/noisy-medical-document-images-ocr.synthetic-australian-medical-documents-sample
Synthetic Australian Medical Documents - Sample
A 50-document free sample of a 5,000-document library of synthetic Australian medical PDFs. PHI-free. Modelled on Australian healthcare documentation. Pre-labelled with structured ground truth and pixel-precise bounding boxes. Released under CC-BY-NC 4.0 for evaluation and non-commercial research.
See Pricing & licensing below.
What's in this sample
Field
Value
Documents
50
Document types
29 (of 45 in full… See the full description on the dataset page: https://huggingface.co/datasets/RootCauseAnalytics/synthetic-australian-medical-documents-sample.document-processing-benchmark
Document Processing Benchmark
8 public document datasets (receipts, invoices, forms, bank statements,
multi-page docs, contracts) normalized into one parquet schema. Each row
has the document, ground-truth annotations, and per-row token/latency/cost
numbers from real API calls to one or more reference models. You can
read off a target's cost/latency/quality without re-running it.
from datasets import load_dataset
ds = load_dataset("thoughtworks/document-processing-benchmark"… See the full description on the dataset page: https://huggingface.co/datasets/thoughtworks/document-processing-benchmark.document-classification-benchmark
Document Classification Benchmark (open-vocab, zero-shot)
Given a document image and an arbitrary set of text labels, which one is right? A held-out, zero-shot,
open-vocabulary evaluation for document-type classification — labels are supplied at inference, not baked
into a head. Test split only; not for training. Every image is drawn from a permissively-licensed,
redistributable source.
Powers the
document-classification-leaderboard
and evaluates document-classification-v2… See the full description on the dataset page: https://huggingface.co/datasets/nutrientdocs/document-classification-benchmark.rvl-cdip-document-classification
rvl-cdip-document-classification
This dataset is created from original aharley/rvl_cdip dataset using this notebook
Dataset Summary
This dataset consists of 8992 grayscale images in 16 classes, with 562 images per class.
There are 8000 training images(500 image per class) and 992 test images(62 images per class).
The images are sized so their largest dimension does not exceed 1000 pixels.
Document-Type-Detection
Document-Type-Detection
Dataset Summary
The Document-Type-Detection dataset is a large-scale image classification dataset consisting of scanned or photographed document images. Each image is categorized into one of nine document types. This dataset is ideal for training document classification models in finance, administration, OCR, and automation workflows.
Supported Tasks
Multiclass Document Classification
Classify an input document image into one of the… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Document-Type-Detection.CMDS_Multimodal_Document
Dataset Card for Cyrillic Multimodel Document (CMDS)
This is the dataset consists of 3789 pairs of images and text across 31 categories downloaded from the Bulgarian ministry of finance
Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Supported Tasks and Leaderboards
Uses this dataset for downstream task like Document Classification, Image Classification or Text Classification… See the full description on the dataset page: https://huggingface.co/datasets/sitloboi2012/CMDS_Multimodal_Document.document-signature-stamp-dataset
Document Signature and Stamp Dataset
A page-level document image dataset for detecting signatures and stamps in Vietnamese administrative and contract documents.
Dataset structure
samples/: 5,714 sample directories.
manifests/page_samples.csv: dataset index with source page, path, and label.
Labels: signature, stamp, both, and none.
Each sample directory contains a rendered document page, metadata, and extracted transparent object layers when applicable.… See the full description on the dataset page: https://huggingface.co/datasets/hvpu/document-signature-stamp-dataset.muninn-ww1-documents
Muninn WWI Documents (CEF Attestation Papers & War Diaries)
A tabular conversion of the document records in the Muninn Project's
World War I Linked Open Data archive (https://rdf.muninn-project.org/). The Muninn Project is a
research project that extracts structured data from digitized WWI-era archival documents.
The dataset covers 28,742 documents — 28,718 Canadian Expeditionary Force (CEF) attestation
papers
(enlistment forms of individual soldiers) and 24 Canadian unit war… See the full description on the dataset page: https://huggingface.co/datasets/biglam/muninn-ww1-documents.