DocLayNet
DocLayNet-v1.2
Dataset Card for DocLayNet v1.2
Dataset Summary
This dataset is an extention of the original DocLayNet dataset which embeds the PDF files of the document images inside a binary column.
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
Human Annotation: DocLayNet is… See the full description on the dataset page: https://huggingface.co/datasets/docling-project/DocLayNet-v1.2.DocLayNet-v1.1
Dataset Card for DocLayNet v1.1
Dataset Summary
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
Human Annotation: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of… See the full description on the dataset page: https://huggingface.co/datasets/docling-project/DocLayNet-v1.1.doclaynet_processed
Dataset Card for "doclaynet_processed"
Clean version of DocLayNet ready for finetuning.
DocLayNet-baseAccurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present \textit{DocLayNet}, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide smallline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10\% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet-trained models are more robust and thus the preferred choice for general-purpose document-layout analysis.DocLayNetDocLayNet is a human-annotated document layout segmentation dataset from a broad variety of document sources.doclaynet_bench