README.md

metadata

license: other
pretty_name: OpenPSS (community mirror)
tags:
  - page-stream-segmentation
  - document-ai
  - document-boundary-detection
  - benchmark
  - mirror
task_categories:
  - image-classification
dataset_info:
  - config_name: LONG
    features:
      - name: stream_id
        dtype: string
      - name: position
        dtype: int32
      - name: image
        dtype: image
      - name: text
        dtype: string
      - name: label
        dtype: int8
    splits:
      - name: train
        num_bytes: 998698445
        num_examples: 63815
      - name: test
        num_bytes: 405971545
        num_examples: 25676
    download_size: 1334166455
    dataset_size: 1404669990
  - config_name: SHORT
    features:
      - name: stream_id
        dtype: string
      - name: position
        dtype: int32
      - name: image
        dtype: image
      - name: text
        dtype: string
      - name: label
        dtype: int8
    splits:
      - name: train
        num_bytes: 976671526
        num_examples: 40715
      - name: test
        num_bytes: 293561622
        num_examples: 11462
    download_size: 1224066290
    dataset_size: 1270233148
configs:
  - config_name: LONG
    data_files:
      - split: train
        path: LONG/train-*
      - split: test
        path: LONG/test-*
  - config_name: SHORT
    data_files:
      - split: train
        path: SHORT/train-*
      - split: test
        path: SHORT/test-*

OpenPSS — community mirror

⚠️ This is a redistribution (mirror) of the OpenPSS benchmark, not our own work. It is hosted for availability and reproducibility. All credit belongs to the original authors. If you are an author or rights-holder and would like any change or removal, please open a discussion here or contact us.

Original work

OpenPSS: An Open Page Stream Segmentation Benchmark — Ruben van Heusden, Jaap Kamps, Maarten Marx (University of Amsterdam, IRLab), TPDL 2024 (DOI 10.1007/978-3-031-72437-4_24). Builds on WooIR (van Heusden, Kamps, Marx, SIGIR ICTIR 2022).

What this is

Page Stream Segmentation (PSS): given a stream of concatenated scanned pages, recover the original document boundaries. OpenPSS provides two datasets — SHORT and LONG. The source documents are Dutch government records released under the Freedom of Information Act (Wet open overheid / Woo), which are public records.

This is a self-contained, HuggingFace-native reformatting of the original OpenPSS release (page images embedded — no external png.zip to unpack), exposed as two loadable configs with train/test splits:

from datasets import load_dataset
short = load_dataset("nutrientdocs/openpss-mirror", "SHORT")   # or "LONG"
# columns: stream_id (str), position (int, 1-indexed page in the stream),
#          image (PIL), text (OCR), label (int: 1 = starts a new document / boundary, else 0)

A stream's ground-truth boundaries are the label column ordered by position; label[0] (page 1) is 1 by construction. Content is faithful to the original OpenPSS SHORT/LONG; only the packaging is changed.

Citation

@inproceedings{vanHeusden2024OpenPSS,
  title     = {OpenPSS: An Open Page Stream Segmentation Benchmark},
  author    = {van Heusden, Ruben and Kamps, Jaap and Marx, Maarten},
  booktitle = {Theory and Practice of Digital Libraries (TPDL)},
  year      = {2024}, doi = {10.1007/978-3-031-72437-4_24}
}

License / terms

The underlying documents are Dutch FOIA (Woo) public records; OpenPSS is distributed as an open benchmark. This mirror redistributes the material under those terms — please consult the original publication for authoritative licensing. Attribution to the original authors is required.

Mirror maintained by Nutrient (nutrientdocs) to keep the OpenPSS benchmark reliably available and to make our page-stream-segmentation results reproducible.