{"url":"/dataset/wost","name":"WOST","full_name":null,"description_markdown":"The Weakly Occluded Scene Text (WOST) dataset is a public dataset for scene text segmentation. It is used to generate pixel-level annotations in scene text images 1. The dataset is designed to contain weakly annotated images, which means that the images are not fully annotated with pixel-level labels.","description_withheld":null,"homepage":"","introduced_date":"2021-08-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/from-two-to-one-a-new-scene-text-recognizer","title":"From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network","first_author":"Yuxin Wang","url":null},"license":null,"modalities":[],"tasks":[{"name":"Scene Text Recognition","url":"/task/scene-text-recognition","datasets_with_task":"/datasets/task/scene-text-recognition"}],"languages":[],"variants":["WOST"],"data_loaders":[],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/scene-text-recognition-on-wost","task":"Scene Text Recognition","dataset_variant":"WOST","rows":5,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"CLIP4STR-H (DFN-5B)","paper":"/paper/clip4str-a-simple-baseline-for-scene-text-1","metrics":{"1:1 Accuracy":"90.9"},"code_links":[{"title":"VamosC/CLIP4STR","url":"https://github.com/VamosC/CLIP4STR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/clip4str-a-simple-baseline-for-scene-text-1","title":"CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model","date":"2023-05-23","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/self-supervised-character-to-character","title":"Self-supervised Character-to-Character Distillation for Text Recognition","date":"2022-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":10,"samples_ran":3,"samples_unverified":7,"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."}