{"url":"/dataset/letter","name":"Letter","full_name":"Letter Recognition Data Set","description_markdown":"Letter Recognition Data Set is a handwritten digit dataset. The task is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15.\r\n\r\nSource: [UCL Machine Learning Repository Letter Recognition](https://archive.ics.uci.edu/ml/datasets/Letter+Recognition)\r\nImage Source: [http://www.cs.uu.nl/docs/vakken/mpr/Frey-Slate.pdf](http://www.cs.uu.nl/docs/vakken/mpr/Frey-Slate.pdf)","description_withheld":null,"homepage":"https://archive.ics.uci.edu/ml/datasets/Letter+Recognition","introduced_date":"1991-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Letter Recognition Using Holland-Style Adaptive Classifiers","first_author":null,"url":"https://doi.org/10.1007/BF00114162"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"},{"name":"Core set discovery","url":"/task/core-set-discovery","datasets_with_task":"/datasets/task/core-set-discovery"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LetterA-J","Letter"],"data_loaders":[],"num_papers_in_archive":49,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-clustering-on-lettera-j","task":"Image Clustering","dataset_variant":"LetterA-J","rows":2,"metrics":["Accuracy","NMI"],"first_row_in_archive_order":{"model":"DDC-DA","paper":"/paper/deep-density-based-image-clustering","metrics":{"Accuracy":"0.691","NMI":"0.629"},"code_links":[{"title":"Yazhou-Ren/DDC","url":"https://github.com/Yazhou-Ren/DDC"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/core-set-discovery-on-letter","task":"Core set discovery","dataset_variant":"Letter","rows":1,"metrics":["F1(10-fold)"],"first_row_in_archive_order":{"model":"EvoCore","paper":"/paper/uncovering-coresets-for-classification-with","metrics":{"F1(10-fold)":"65.9"},"code_links":[{"title":"pietrobarbiero/meco","url":"https://github.com/pietrobarbiero/meco"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/uncovering-coresets-for-classification-with","title":"Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms","date":"2020-02-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-density-based-image-clustering","title":"Deep Density-based Image Clustering","date":"2018-12-11","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}