{"url":"/dataset/poie","name":"POIE","full_name":"Products for OCR and Information Extraction","description_markdown":"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 mul\u0002tiple 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.\r\n\r\nfrom https://github.com/jfkuang/cfam","description_withheld":null,"homepage":"https://github.com/jfkuang/cfam","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Key Information Extraction","url":"/task/key-information-extraction","datasets_with_task":"/datasets/task/key-information-extraction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["POIE"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"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."}