Consists of a dataset with 1000 whole scanned receipt images and annotations for the competition on scanned receipts OCR and key information extraction (SROIE). Image source: https://arxiv.org/pdf/2103.10213.pdf

OCR is inevitably linked to NLP since its final output is in text. Advances in document intelligence are driving the need for a unified technology that integrates OCR with various NLP tasks, especially semantic parsing. Since OCR and semantic parsing have been studied as separate tasks so far, the datasets for each task on their own are rich, while those for the integrated post-OCR parsing tasks are relatively insufficient. In this study, we publish a consolidated dataset for receipt parsing as the first step towards post-OCR parsing tasks. The dataset consists of thousands of Indonesian receipts, which contains images and box/text annotations for OCR, and multi-level semantic labels for parsing. The proposed dataset can be used to address various OCR and parsing tasks.

EPHOIE is a fully-annotated dataset which is the first Chinese benchmark for both text spotting and visual information extraction. EPHOIE consists of 1,494 images of examination paper head with complex layouts and background, including a total of 15,771 Chinese handwritten or printed text instances. Source: Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution Image source: https://github.com/HCIILAB/EPHOIE

Kleister NDA is a dataset for Key Information Extraction (KIE). The dataset contains a mix of scanned and born-digital long formal English-language documents. For this datasets, an NLP system is expected to find or infer various types of entities by employing both textual and structural layout features. The Kleister NDA dataset has 540 Non-disclosure Agreements, with 3,229 unique pages and 2,160 entities to extract.

SIBR是面向自然场景视觉信息抽取的数据集。 1）SIBR总的有1000张图片，400张测试，600张训练，包括中文、英文两种语言。 2）包含images.zip、label.zip、train.txt、test.txt四个文件，images.zip、label.zip中包含所有图片和标签，通过train.txt和test.txt区分训练和测试。 3）标注规则与FUNSD、XFUND一致，实体类别包括header、question、answer、other四类，但与之不同的是除了提供link id之外，还标注了link type。link type包括inter以及intra两种，inter表示实体间的link，也即是一对kv对之间的link；而intra表示同一个实体内部segment之间的link。通过intra将多行文字组成一个实体，通过inter将实体组成kv对。

DocILE is a large dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It contains 6.7k annotated business documents, 100k synthetically generated documents, and nearly 1M unlabeled documents for unsupervised pre-training. The dataset has been built with knowledge of domain- and task-specific aspects, resulting in the following key features: i) annotations in 55 classes, which surpasses the granularity of previously published key information extraction datasets by a large margin ii) Line Item Recognition represents a highly practical information extraction task, where key information has to be assigned to items in a table iii) documents come from numerous layouts and the test set includes zero- and few-shot cases as well as layouts commonly seen in the training set Source: DocILE Benchmark for Document Information Localization and Extraction Image Source : https://arxiv.org/pdf/2302.05658v1.pdf

RFUND is a relabeled version of FUNSD and XFUND datasets, tackling the following issues in their original annotations: 1. Entity (block) level OCR results. Real-world OCR engines usually produce line-level results, while the annotations in FUNSD and XFUND are at the entity (block) level. Text lines within the same entity are aggregated and serialized in human reading order, simplifying the task scope and failing to reflect real-world challenges. 2. Inconsistent labelling granularity. In FUNSD, while most contents are annotated at the entity level, multi-line entities with first-line indentation are annotated separately, in which the first line is split out and the rest are aggregated. XFUND exhibits variable granularity in annotations, with some contents labelled at the entity level and others at the line level. Such inconsistent labelling standards can hinder model training. 3. Erroneous category annotations. Entities in FUNSD/XFUND are categorized as "header", "question", "answer" and "other". We observed that certain entities in both FUNSD and XFUND have category labels that differ from human understanding. In the following example, the answer entity “Client confirmed agreement ...” was labelled as other, while the other entity “CONFIDENTIAL” was labelled as the question.

English subset of RFUND

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

The paper used 500 scanned Electronic Theses and Dissertation cover pages (i.e., front pages). The dataset contains several intermediate datasets, briefly discussed in the paper.

Description We propose a new database for information extraction from historical handwritten documents. The corpus includes 5,393 finding aids from six different series, dating from the 18th-20th centuries. Finding aids are handwritten documents that contain metadata describing older archives. They are stored in the National Archives of France and are used by archivists to identify and find archival documents. Each document is annotated at page-level, and contains seven fields to retrieve. The localization of each field is not available in such a way that this dataset encourages research on segmentation-free systems for information extraction. The dataset is available at https://zenodo.org/record/7868059 ## Details for each series and entity type | Series | Train | Validation | Test | Total (%) | | ------------------- | ----- | ---------- | ---- | --------: | | E series | 322 | 64 | 79 | 8.6 | | L series | 38 | 8 | 4 | 0.9 | | M series | 128 | 21 | 27 | 3.3 | | X1a series | 2209 | 491 | 469 | 58.8 | | Y series | 940 | 205 | 196 | 24.9 | | Douët s'Arcq series | 141 | 22 | 29 | 3.5 | | Total | 3778 | 811 | 804 | 100 | | Entities | Train | Validation | Test | Total (%) | | -------------- | ----- | ---------- | ----- | --------: | | date | 8406 | 1814 | 1799 | 10.4 | | title | 35531 | 7495 | 8173 | 44.5 | | serie | 3168 | 664 | 676 | 3.9 | | analysis | 25988 | 5130 | 5602 | 31.9 | | volume_number | 3913 | 808 | 813 | 4.8 | | article_number | 3181 | 665 | 678 | 3.9 | | arrangement | 644 | 122 | 153 | 0.8 | | Total | 80831 | 16698 | 17894 | 100 | ## Data encoding Transcriptions with entities are encoded in the labels.json JSON file. Special tokens are used to represent named entities. Please not that there are only opening NER tokens: each entity spans all words until the next entity starts. | Entities | Special token | Symbol unicode | | -------------- | ------------- | -------- | | date | ⓓ | \u24d3 | | title | ⓘ | \u24d8 | | serie | ⓢ | \u24e2 | | analysis | ⓒ | \u24d2 | | volume_number | ⓟ | \u24df | | article_number | ⓐ | \u24d0 | | arrangement | ⓥ | \u24e5 | # Cite us! The dataset is presented in details in the following article: bib @article{simara2023, author = {Solène Tarride and Mélodie Boillet and Jean-François Moufflet and Christopher Kermorvant}, title = {SIMARA: a database for key-value information extraction from full-page handwritten documents}, year = {2023}, journal={Proceedings of the 17th International Conference on Document Analysis and Recognition}, }

Products for OCR and Information Extraction (POIE) dataset derives from camera images of various products in the real world. The images are carefully selected and manually annotated. Our labeling team consists of 8 experienced labelers. We first crop the nutrition tables from product images and adopt multiple commercial OCR engines (Azure and Baidu OCR) for pre-labeling. Then we use LabelMe to manually check the annotation of the location as well as transcription of every text box, and the values of entities for all the text in the images and repaired the OCR errors found. After discarding low-quality and blurred images, we obtain 3,000 images with 111,155 text instances. from https://github.com/jfkuang/cfam

The dataset contains the training and test data for the SOftware Mention Detection challenge. The data is derived from the SoMeSci Knowledge Graph of software mentions. Subtask 1 deals with the recognition of software mentions and the classification of mention (e.g. Usage, Creation,...) and software types (e.g. Application, PlugIn,...) at the same time Subtask 2 requires the recognition of additional meta data of software mentions (e.g. Version, Developer, URL,...) Subtask 3 deals with extracting the relations between the different entities of interest (e.g. Version_of, License_of,...) A detailed description of the dataset including the creation and a baseline for the different subtasks can be found in the following article D. Schindler, F. Bensmann, S. Dietze, and F. Krüger, “SoMeSci—A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles,” in Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM ’21), Virtual Event, QLD, Australia: Association for Computing Machinery, Nov. 2021. doi: 10.1145/3459637.3482017.

Artificial Relationships in Fiction ## Dataset Description Artificial Relationships in Fiction (ARF) is a synthetically annotated dataset for Relation Extraction (RE) in fiction, created from a curated selection of literary texts sourced from Project Gutenberg. The dataset captures the rich, implicit relationships within fictional narratives using a novel ontology and GPT-4o for annotation. ARF is the first large-scale RE resource designed specifically for literary texts, advancing both NLP model training and computational literary analysis. ## Dataset Configurations and Features ### Configurations - fiction_books: Metadata-rich corpus of 6,322 public domain fiction books (1850–1950) with inferred author gender and thematic categorization. - fiction_books_in_chunks: Books segmented into 5-sentence chunks (5.96M total), preserving narrative coherence via 1-sentence overlap. - fiction_books_with_relations: A subset of 95,475 text chunks annotated with 128,000+ relationships using GPT-4o and a fiction-specific ontology. ### 1. Configuration: fiction_books - Description: Contains the full text and metadata of 6,322 English-language fiction books from Project Gutenberg. - Features: - book_id: Unique Project Gutenberg ID. - title: Title of the book. - author: Author name. - author_birth_year / author_death_year: Author lifespan. - release_date: PG release date. - subjects: List of thematic topics (mapped to 51 standardized themes). - gender: Inferred author gender (via GPT-4o). - text: Cleaned full book text. - Use Case: Supports thematic and demographic analysis of literary texts. ### 2. Configuration: fiction_books_in_chunks - Description: Each book is segmented into overlapping five-sentence text chunks to enable granular NLP analysis. - Features: - book_id, chunk_index: Book and chunk identifiers. - text_chunk: Five-sentence excerpt from the book. - Use Case: Facilitates sequence-level tasks like coreference resolution or narrative progression modeling. ### 3. Configuration: synthetic_relations_in_fiction_books (ARF) - Description: This subset corresponds to the Artificial Relationships in Fiction (ARF) dataset proposed in the LaTeCH-CLfL 2025 paper "Artificial Relationships in Fiction: A Dataset for Advancing NLP in Literary Domains". - Features: - book_id, chunk_index: Identifiers. - text_chunk: Five-sentence text segment. - relations: A list of structured relation annotations, each containing: - entity1, entity2: Text spans. - entity1Type, entity2Type: Entity types based on ontology. - relation: Relationship type. - Use Case: Ideal for training and evaluating RE models in fictional narratives, studying character networks, and generating structured data from literary texts. ## ARF Dataset Structure (config 'synthetic_relations_in_fiction_books') Each annotated relation is formatted as: json { "entity1": "Head Entity text", "entity2": "Tail Entity text", "entity1Type": "Head entity type", "entity2Type": "Tail entity type", "relation": "Relation type" } Example: json { "entity1": "Vortigern", "entity2": "castle", "entity1Type": "PER", "entity2Type": "FAC", "relation": "owns" } ### Entity Types (11) | Entity Type | Description | |-------------|-------------| | PER | Person or group of people | | FAC | Facility – man-made structures for human use | | LOC | Location – natural or loosely defined geographic regions | | WTHR | Weather – atmospheric or celestial phenomena | | VEH | Vehicle – transport devices (e.g., ship, carriage) | | ORG | Organization – formal groups or institutions | | EVNT | Event – significant occurrences in narrative | | TIME | Time – chronological or historical expressions | | OBJ | Object – tangible items in the text | | SENT | Sentiment – emotional states or feelings | | CNCP | Concept – abstract ideas or motifs | ### Relation Types (48) | Relation Type | Entity 1 Type | Entity 2 Type | Description | |----------------------|------------------|-------------------|-------------------------------------------| | parent_father_of | PER | PER | Father relationship | | parent_mother_of | PER | PER | Mother relationship | | child_of | PER | PER | Child to parent | | sibling_of | PER | PER | Sibling relationship | | spouse_of | PER | PER | Spousal relationship | | relative_of | PER | PER | Extended family relationship | | adopted_by | PER | PER | Adopted by another person | | companion_of | PER | PER | Companionship or ally | | friend_of | PER | PER | Friendship | | lover_of | PER | PER | Romantic relationship | | rival_of | PER | PER | Rivalry | | enemy_of | PER/ORG | PER/ORG | Hostile or antagonistic relationship | | inspires | PER | PER | Inspires or motivates | | sacrifices_for | PER | PER | Makes a sacrifice for | | mentor_of | PER | PER | Mentorship or guidance | | teacher_of | PER | PER | Formal teaching relationship | | protector_of | PER | PER | Provides protection to | | employer_of | PER | PER | Employment relationship | | leader_of | PER | ORG | Leader of an organization | | member_of | PER | ORG | Membership in an organization | | lives_in | PER | FAC/LOC | Lives in a location | | lived_in | PER | TIME | Historically lived in | | visits | PER | FAC | Visits a facility | | travel_to | PER | LOC | Travels to a location | | born_in | PER | LOC | Birthplace | | travels_by | PER | VEH | Travels by a vehicle | | participates_in | PER | EVNT | Participates in an event | | causes | PER | EVNT | Causes an event | | owns | PER | OBJ | Owns an object | | believes_in | PER | CNCP | Believes in a concept | | embodies | PER | CNCP | Embodies a concept | | located_in | FAC | LOC | Located in a place | | part_of | FAC/LOC/ORG | FAC/LOC/ORG | Part of a larger entity | | owned_by | FAC/VEH | PER | Owned by someone | | occupied_by | FAC | PER | Occupied by someone | | used_by | FAC | ORG | Used by an organization | | affects | WTHR | LOC/EVNT | Weather affects location or event | | experienced_by | WTHR | PER | Weather experienced by someone | | travels_in | VEH | LOC | Vehicle travels in a location | | based_in | ORG | LOC | Organization based in a location | | attended_by | EVNT | PER | Event attended by person | | ends_in | EVNT | TIME | Event ends at a time | | occurs_in | EVNT | LOC/TIME | Event occurs in a place or time | | features | EVNT | OBJ | Event features an object | | stored_in | OBJ | LOC/FAC | Object stored in a place | | expressed_by | SENT | PER | Sentiment expressed by person | | used_by | OBJ | PER | Object used by person | | associated_with | CNCP | EVNT | Concept associated with event | ## Dataset Statistics | Metric | Value | |----------------------------|------------| | Books | 96 | | Authors | 91 | | Gender Ratio (M/F) | 55% / 45% | | Subgenres | 51 | | Annotated Chunks | 95,475 | | Relations per Chunk | 1.34 avg | | Chunks with No Relations | 35,230 | | Total Relations | ~128,000 | ## Methodology - Source Texts: English-language fiction from PG bookshelves: Fiction, Children & YA, Crime/Mystery. - Annotation Model: GPT-4o via custom prompt integrating strict ontologies. - Sampling: Balanced author gender and thematic distributions. - Ontology Adherence: <0.05% deviation for entities; 2.95% for relations. - Format: Structured JSON, optimized for NLP pipelines. ## Applications - Fine-tuning RE Models: Adapt models to literary domains with implicit, evolving relationships. - Computational Literary Studies: Analyze character networks, thematic evolution, and genre patterns. - Creative AI: Enhance AI-driven storytelling, character consistency, and world-building tools. ## Citation If you use this dataset in your research, please cite: bibtex @inproceedings{christou-tsoumakas-2025-artificial, title = "Artificial Relationships in Fiction: A Dataset for Advancing {NLP} in Literary Domains", author = "Christou, Despina and Tsoumakas, Grigorios", editor = "Kazantseva, Anna and Szpakowicz, Stan and Degaetano-Ortlieb, Stefania and Bizzoni, Yuri and Pagel, Janis", booktitle = "Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025)", month = may, year = "2025", address = "Albuquerque, New Mexico", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2025.latechclfl-1.13/", pages = "130--147", ISBN = "979-8-89176-241-1" }