OPRB: Occluded Pages Restoration Benchmark

OPRB is a large-scale benchmark dataset for evaluating document page restoration from physical occlusion artifacts. Given a document page that has been partially obscured (by stamps, ink, whitener fluid, dust, scribbles, etc.), the task is to recover the original token-level annotations — restoring the positions, fonts, and semantic categories of words that have been corrupted or hidden by occlusion.

The dataset is derived from DocBank, a weakly-supervised dataset of arXiv papers with fine-grained token-level layout annotations. OPRB adds synthetic occlusion to DocBank pages, providing paired (occluded ↔ ground-truth) annotation files for training and evaluating restoration models.

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Dataset Structure

Each sample consists of four paired files sharing the same base name:

File type	Location	Format	Description
Occluded image	{split}/{category}/img/	.jpg	Page image after occlusion is applied
Occluded annotation	{split}/{category}/annots/	.txt	Token-level annotation of the occluded page
Ground truth image	{split}/{category}/img_gt/	.jpg	Original clean page image
Ground truth annotation	{split}/{category}/gt/	.txt	Token-level annotation of the clean page

The two occlusion categories are:

• text/ — occlusions that primarily overlap with text regions
• nontext/ — occlusions that primarily overlap with non-text (figure, table, equation) regions

Repository layout

Each directory is stored as a gzip-compressed tar archive to keep the repository compact. All archives together total approximately 9 GB (images are stored as JPEG so they compress minimally; annotations compress ~10:1).

OPRB/
├── README.md
├── train/
│   ├── text/
│   │   ├── img.tar.gz      
│   │   ├── img_gt.tar.gz    
│   │   ├── annots.tar.gz   
│   │   └── gt.tar.gz      
│   └── nontext/
│       ├── img.tar.gz         
│       ├── img_gt.tar.gz   
│       ├── annots.tar.gz  
│       └── gt.tar.gz        
└── test/
    ├── text/
    │   ├── img.tar.gz       
    │   ├── img_gt.tar.gz    
    │   ├── annots.tar.gz    
    │   └── gt.tar.gz       
    └── nontext/
        ├── img.tar.gz      
        ├── img_gt.tar.gz    
        ├── annots.tar.gz    
        └── gt.tar.gz       

To extract an archive:

mkdir -p train/text/img
tar -xzf train_text_img.tar.gz -C train/text/img/

Split statistics

Split	text samples	nontext samples	Total samples
train	17,212	3,866	21,078
test	6,000	3,000	9,000
Total	23,212	6,866	30,078

Each sample = 1 occluded image + 1 clean image + 1 occluded annotation + 1 clean annotation.

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File Format

Each annotation file is a tab-separated text file where every row represents one token (word or character) on the page.

Columns

#	Field	Description
1	token	The text token (word, character, or symbol)
2–9	x1 y1 x2 y2 x3 y3 x4 y4	Eight bounding-box coordinates describing the rotated quadrilateral enclosing the token (in page pixel space). For axis-aligned boxes, all four corners collapse to a rectangle.
10	font_name	Name of the font used to render the token (e.g., CMSY10, NimbusRomNo9L-Regu)
11	category	Semantic document-element category. One of: title, author, abstract, paragraph, section, equation, figure, table, caption, reference, footer, date, email, list

File Naming Convention

{OcclusionType}__{density}__{batchId}.tar_{arXivId}.gz_{paperName}_{pageIndex}_ori.txt

Part	Example	Meaning
OcclusionType	White_Whitener	Type of occlusion applied (see table below)
density	2p0pct	Area fraction of the page that is occluded (2p0pct = 2.0%)
batchId	105	DocBank source batch identifier
arXivId	1804.06143.gz	arXiv paper ID
paperName	MassiveMIMO	Shortened paper/file name
pageIndex	10	Zero-based page index within the paper

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Occlusion Types

Type	Category	Description
Black_Scribble	Dark	Handwritten-style black scribble marks overlaid on the page
Black_Ink	Dark	Solid black ink blotches or strokes
Through_Stamp	See Through	Stamp-like markings that partially show through (semi-transparent)
Through_Dust	See Through	Fine dust or speckle texture simulating aged/dirty documents
White_Burnt	Light	Burnt / bleached white patches obscuring text
White_Whitener	Light	White correction-fluid (liquid paper) covering text
Sim	Synthetic Scribble	Simulated scribble artifacts
Mixed	Combined	A combination of two or more occlusion types on the same page

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How to Load

Step 1 — Download and extract archives

from huggingface_hub import hf_hub_download
import tarfile, os

def download_and_extract(repo_id, archive_repo_path, extract_to):
    """Download a tar.gz archive from the Hub and extract it locally."""
    os.makedirs(extract_to, exist_ok=True)
    local_path = hf_hub_download(
        repo_id=repo_id,
        filename=archive_repo_path,
        repo_type="dataset",
    )
    with tarfile.open(local_path, "r:gz") as tar:
        tar.extractall(extract_to)

REPO = "kpurkayastha/OPRB"

# Download train text split (images + annotations)
download_and_extract(REPO, "train/text/img.tar.gz",    "data/train/text/img")
download_and_extract(REPO, "train/text/img_gt.tar.gz", "data/train/text/img_gt")
download_and_extract(REPO, "train/text/annots.tar.gz", "data/train/text/annots")
download_and_extract(REPO, "train/text/gt.tar.gz",     "data/train/text/gt")

Step 2 — Load a paired sample

from pathlib import Path
from PIL import Image

def load_annotation(filepath):
    """Load an OPRB annotation file into a list of token dicts."""
    tokens = []
    with open(filepath, encoding="utf-8") as f:
        for line in f:
            parts = line.rstrip("\n").split("\t")
            if len(parts) < 11:
                continue
            tokens.append({
                "token":    parts[0],
                "bbox":     [int(x) for x in parts[1:9]],  # 8 rotated-bbox coords
                "font":     parts[9],
                "category": parts[10],
            })
    return tokens

base = "White_Whitener__2p0pct__105.tar_1804.06143.gz_MassiveMIMO_10_ori"

# Occluded inputs
occluded_img   = Image.open(f"data/train/text/img/{base}.jpg")
occluded_annot = load_annotation(f"data/train/text/annots/{base}.txt")

# Ground truth targets
clean_img   = Image.open(f"data/train/text/img_gt/{base}.jpg")
clean_annot = load_annotation(f"data/train/text/gt/{base}.txt")

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Provenance

OPRB is built on top of DocBank (Li et al., 2020), a large-scale dataset for document layout analysis containing 500K document pages from arXiv papers with token-level bounding-box and semantic category annotations. Synthetic occlusions were applied programmatically to DocBank pages to produce the OPRB training and test splits.

@dataset{oprb2026,
  title  = {OPRB: Occluded Pages Restoration Benchmark},
  author = {Purkayastha, Kunal},
  year   = {2026},
  url    = {https://huggingface.co/datasets/kpurkayastha/OPRB},
}

@inproceedings{li2020docbank,
  title     = {DocBank: A Benchmark Dataset for Document Layout Analysis},
  author    = {Li, Minghao and Xu, Yiheng and Cui, Lei and Huang, Shaohan and Wei, Furu and Li, Zhe and Zhou, Ming},
  booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
  year      = {2020},
}

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License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to share and adapt the material for any purpose, provided appropriate credit is given.

The underlying DocBank annotations are also available under CC BY 4.0.