{"url":"/dataset/cord","name":"CORD","full_name":"Consolidated Receipt Dataset for Post-OCR Parsing","description_markdown":"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.","description_withheld":null,"homepage":"https://github.com/clovaai/cord","introduced_date":"2019-09-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/cord-a-consolidated-receipt-dataset-for-post","title":"CORD: A Consolidated Receipt Dataset for Post-OCR Parsing","first_author":"Seunghyun Park","url":null},"license":{"name":"Creative Commons Attribution 4.0 International License","url":"https://github.com/clovaai/cord/blob/master/LICENSE-CC-BY"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Named Entity Recognition (NER)","url":"/task/named-entity-recognition-ner","datasets_with_task":"/datasets/task/named-entity-recognition-ner"},{"name":"Key Information Extraction","url":"/task/key-information-extraction","datasets_with_task":"/datasets/task/key-information-extraction"}],"languages":[{"name":"Indonesian","url":"/datasets/language/indonesian"}],"variants":["CORD"],"data_loaders":[{"repo":"https://github.com/AyeshaAmjad0828/Unstructured-Data-Extraction","url":"https://github.com/AyeshaAmjad0828/Unstructured-Data-Extraction","frameworks":[]}],"num_papers_in_archive":100,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/key-information-extraction-on-cord","task":"Key Information Extraction","dataset_variant":"CORD","rows":9,"metrics":["F1"],"first_row_in_archive_order":{"model":"RORE (GeoLayoutLM)","paper":"/paper/modeling-layout-reading-order-as-ordering","metrics":{"F1":"98.52"},"code_links":[{"title":"chongzhangFDU/ROOR","url":"https://github.com/chongzhangFDU/ROOR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/modeling-layout-reading-order-as-ordering","title":"Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding","date":"2024-09-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/reading-order-matters-information-extraction","title":"Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction","date":"2023-10-17","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/layoutmask-enhance-text-layout-interaction-in","title":"LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding","date":"2023-05-30","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/geolayoutlm-geometric-pre-training-for-visual","title":"GeoLayoutLM: Geometric Pre-training for Visual Information Extraction","date":"2023-04-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/layoutlmv3-pre-training-for-document-ai-with","title":"LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking","date":"2022-04-18","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/lilt-a-simple-yet-effective-language","title":"LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding","date":"2022-02-28","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/layoutlmv2-multi-modal-pre-training-for","title":"LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding","date":"2020-12-29","rows_on_this_dataset":2,"code_links":9,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}