{"id":20360605,"url":"https://github.com/davidgasquez/letter-recognition","last_synced_at":"2026-03-05T16:48:52.167Z","repository":{"id":24486185,"uuid":"27890664","full_name":"davidgasquez/letter-recognition","owner":"davidgasquez","description":":information_source: Application of Machine Learning techniques to identify randomly distorted capital letters in the English alphabet.","archived":false,"fork":false,"pushed_at":"2015-06-30T14:53:09.000Z","size":436,"stargazers_count":13,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-04-12T03:53:02.730Z","etag":null,"topics":["letter-recognition","machine-learning"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/davidgasquez.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2014-12-11T20:39:02.000Z","updated_at":"2024-06-05T05:40:48.000Z","dependencies_parsed_at":"2022-08-22T18:00:23.693Z","dependency_job_id":null,"html_url":"https://github.com/davidgasquez/letter-recognition","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/davidgasquez%2Fletter-recognition","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/davidgasquez%2Fletter-recognition/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/davidgasquez%2Fletter-recognition/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/davidgasquez%2Fletter-recognition/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/davidgasquez","download_url":"https://codeload.github.com/davidgasquez/letter-recognition/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248514209,"owners_count":21116899,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["letter-recognition","machine-learning"],"created_at":"2024-11-14T23:42:18.323Z","updated_at":"2026-03-05T16:48:52.109Z","avatar_url":"https://github.com/davidgasquez.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"Letter Recognition\n==================\n\nThe objective is to identify each of a large number of black-and-white\nrectangular pixel displays as one of the 26 capital letters in the English\nalphabet. The character images were based on 20 different fonts and each\nletter within these 20 fonts was randomly distorted to produce a file of\n20.000 unique stimuli. Each stimulus was converted into 16 primitive numerical\nattributes (statistical moments and edge counts) which were then scaled to fit\ninto a range of integer values from 0 through 15.\n\n\nData Set Information\n--------------------\n\n - [Letter Recognition Data Set](https://archive.ics.uci.edu/ml/machine-learning-databases/letter-recognition/letter-recognition.names)\n - [Data Folder](https://archive.ics.uci.edu/ml/machine-learning-databases/letter-recognition/)\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eData Set Characteristics\u003c/td\u003e\n    \u003ctd\u003eMultivariate\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eAttribute Characteristics\u003c/td\u003e\n    \u003ctd\u003eInteger\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eNumber of Attributes\u003c/td\u003e\n    \u003ctd\u003e16\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eNumber of Instances\u003c/td\u003e\n    \u003ctd\u003e20.000\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eAssociated Tasks\u003c/td\u003e\n    \u003ctd\u003eClassification\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\nResults\n-------\nWe are going to measure the accuracy rate into the *test*\nsubset(4.000 instances)\n\n| Technique      | Test Rate |\n|----------------|-----------|\n|            LDA |  0.8955   |\n|            QDA |  0.9497   |\n|            KNN |  0.9641   |\n|   Tree(simple) |  0.4799   |\n|        Bagging |  0.9454   |\n| Random Forests |  0.9915   |\n|       Boosting |  0.5805   |\n|            SVM |  0.9487   |\n\nSource\n------\n\nDavid J. Slate\n\nOdesta Corporation;\n\n1890 Maple Ave; Suite 115;\n\nEvanston, IL 60201\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdavidgasquez%2Fletter-recognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdavidgasquez%2Fletter-recognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdavidgasquez%2Fletter-recognition/lists"}