Duplicate from docling-project/DocLayNet

Co-authored-by: Michele Dolfi <dolfim-ibm@users.noreply.huggingface.co>

.gitattributes

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+*.webp filter=lfs diff=lfs merge=lfs -text

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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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+# Translations
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+# Scrapy stuff:
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+
+# Sphinx documentation
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+# PyBuilder
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+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
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+# Spyder project settings
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+# Rope project settings
+.ropeproject
+
+# Mr Developer
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+.pydevproject
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+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
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+
+# Pyre type checker
+.pyre/
+
+### VirtualEnv ###
+# Virtualenv
+# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
+pyvenv.cfg
+.env
+.venv
+env/
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+ENV/
+env.bak/
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+### VisualStudioCode ###
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+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
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+# Windows thumbnail cache files
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+*.stackdump
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DocLayNet.py

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+"""
+Inspired from
+https://huggingface.co/datasets/ydshieh/coco_dataset_script/blob/main/coco_dataset_script.py
+"""
+
+import json
+import os
+import datasets
+import collections
+
+
+class COCOBuilderConfig(datasets.BuilderConfig):
+    def __init__(self, name, splits, **kwargs):
+        super().__init__(name, **kwargs)
+        self.splits = splits
+
+
+# Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{doclaynet2022,
+  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},  
+  doi = {10.1145/3534678.353904},
+  url = {https://arxiv.org/abs/2206.01062},
+  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
+  year = {2022}
+}
+"""
+
+# Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+DocLayNet is a human-annotated document layout segmentation dataset from a broad variety of document sources.
+"""
+
+# Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://developer.ibm.com/exchanges/data/all/doclaynet/"
+
+# Add the licence for the dataset here if you can find it
+_LICENSE = "CDLA-Permissive-1.0"
+
+# Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+
+_URLs = {
+    "core": "https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip",
+}
+
+# Name of the dataset usually match the script name with CamelCase instead of snake_case
+class COCODataset(datasets.GeneratorBasedBuilder):
+    """An example dataset script to work with the local (downloaded) COCO dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIG_CLASS = COCOBuilderConfig
+    BUILDER_CONFIGS = [
+        COCOBuilderConfig(name="2022.08", splits=["train", "val", "test"]),
+    ]
+    DEFAULT_CONFIG_NAME = "2022.08"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("int64"),
+                "image": datasets.Image(),
+                "width": datasets.Value("int32"),
+                "height": datasets.Value("int32"),
+                # Custom fields
+                "doc_category": datasets.Value(
+                    "string"
+                ),  # high-level document category
+                "collection": datasets.Value("string"),  # sub-collection name
+                "doc_name": datasets.Value("string"),  # original document filename
+                "page_no": datasets.Value("int64"),  # page number in original document
+            }
+        )
+        object_dict = {
+            "category_id": datasets.ClassLabel(
+                names=[
+                    "Caption",
+                    "Footnote",
+                    "Formula",
+                    "List-item",
+                    "Page-footer",
+                    "Page-header",
+                    "Picture",
+                    "Section-header",
+                    "Table",
+                    "Text",
+                    "Title",
+                ]
+            ),
+            "image_id": datasets.Value("string"),
+            "id": datasets.Value("int64"),
+            "area": datasets.Value("int64"),
+            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+            "segmentation": [[datasets.Value("float32")]],
+            "iscrowd": datasets.Value("bool"),
+            "precedence": datasets.Value("int32"),
+        }
+        features["objects"] = [object_dict]
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        archive_path = dl_manager.download_and_extract(_URLs)
+        splits = []
+        for split in self.config.splits:
+            if split == "train":
+                dataset = datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "json_path": os.path.join(
+                            archive_path["core"], "COCO", "train.json"
+                        ),
+                        "image_dir": os.path.join(archive_path["core"], "PNG"),
+                        "split": "train",
+                    },
+                )
+            elif split in ["val", "valid", "validation", "dev"]:
+                dataset = datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "json_path": os.path.join(
+                            archive_path["core"], "COCO", "val.json"
+                        ),
+                        "image_dir": os.path.join(archive_path["core"], "PNG"),
+                        "split": "val",
+                    },
+                )
+            elif split == "test":
+                dataset = datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "json_path": os.path.join(
+                            archive_path["core"], "COCO", "test.json"
+                        ),
+                        "image_dir": os.path.join(archive_path["core"], "PNG"),
+                        "split": "test",
+                    },
+                )
+            else:
+                continue
+
+            splits.append(dataset)
+        return splits
+
+    def _generate_examples(
+        # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+        self,
+        json_path,
+        image_dir,
+        split,
+    ):
+        """Yields examples as (key, example) tuples."""
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+        def _image_info_to_example(image_info, image_dir):
+            image = image_info["file_name"]
+            return {
+                "image_id": image_info["id"],
+                "image": os.path.join(image_dir, image),
+                "width": image_info["width"],
+                "height": image_info["height"],
+                "doc_category": image_info["doc_category"],
+                "collection": image_info["collection"],
+                "doc_name": image_info["doc_name"],
+                "page_no": image_info["page_no"],
+            }
+
+        with open(json_path, encoding="utf8") as f:
+            annotation_data = json.load(f)
+            images = annotation_data["images"]
+            annotations = annotation_data["annotations"]
+            image_id_to_annotations = collections.defaultdict(list)
+            for annotation in annotations:
+                image_id_to_annotations[annotation["image_id"]].append(annotation)
+
+        for idx, image_info in enumerate(images):
+            example = _image_info_to_example(image_info, image_dir)
+            annotations = image_id_to_annotations[image_info["id"]]
+            objects = []
+            for annotation in annotations:
+                category_id = annotation["category_id"]  # Zero based counting
+                if category_id != -1:
+                    category_id = category_id - 1
+                annotation["category_id"] = category_id
+                objects.append(annotation)
+            example["objects"] = objects
+            yield idx, example

README.md

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+---
+annotations_creators:
+- crowdsourced
+license: other
+pretty_name: DocLayNet
+size_categories:
+- 10K<n<100K
+tags:
+- layout-segmentation
+- COCO
+- document-understanding
+- PDF
+task_categories:
+- object-detection
+- image-segmentation
+task_ids:
+- instance-segmentation
+---
+
+# Dataset Card for DocLayNet
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Annotations](#annotations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
+- **Repository:** https://github.com/DS4SD/DocLayNet
+- **Paper:** https://doi.org/10.1145/3534678.3539043
+- **Leaderboard:**
+- **Point of Contact:**
+
+### 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:
+
+1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
+2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
+3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
+4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
+5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
+
+### Supported Tasks and Leaderboards
+
+We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
+
+## Dataset Structure
+
+### Data Fields
+
+DocLayNet provides four types of data assets:
+
+1. PNG images of all pages, resized to square `1025 x 1025px`
+2. Bounding-box annotations in COCO format for each PNG image
+3. Extra: Single-page PDF files matching each PNG image
+4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
+
+The COCO image record are defined like this example
+
+```js
+    ...
+    {
+      "id": 1,
+      "width": 1025,
+      "height": 1025,
+      "file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
+
+      // Custom fields:
+      "doc_category": "financial_reports" // high-level document category
+      "collection": "ann_reports_00_04_fancy", // sub-collection name
+      "doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
+      "page_no": 9, // page number in original document
+      "precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
+    },
+    ...
+```
+
+The `doc_category` field uses one of the following constants:
+
+```
+financial_reports,
+scientific_articles,
+laws_and_regulations,
+government_tenders,
+manuals,
+patents
+```
+
+
+### Data Splits
+
+The dataset provides three splits
+- `train`
+- `val`
+- `test`
+
+## Dataset Creation
+
+### Annotations
+
+#### Annotation process
+
+The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
+
+
+#### Who are the annotators?
+
+Annotations are crowdsourced.
+
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
+You can contact us at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com).
+
+Curators:
+- Christoph Auer, [@cau-git](https://github.com/cau-git)
+- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
+- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
+- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
+
+### Licensing Information
+
+License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
+
+
+### Citation Information
+
+
+```bib
+@article{doclaynet2022,
+  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
+  doi = {10.1145/3534678.353904},
+  url = {https://doi.org/10.1145/3534678.3539043},
+  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
+  year = {2022},
+  isbn = {9781450393850},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
+  pages = {3743–3751},
+  numpages = {9},
+  location = {Washington DC, USA},
+  series = {KDD '22}
+}
+```
+
+### Contributions
+
+Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset.