{"id":4093,"date":"2020-10-11T21:51:23","date_gmt":"2020-10-12T01:51:23","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?p=4093"},"modified":"2020-10-11T21:51:24","modified_gmt":"2020-10-12T01:51:24","slug":"cs488-abstract","status":"publish","type":"post","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/2020\/10\/11\/cs488-abstract\/","title":{"rendered":"CS488 &#8211; Abstract"},"content":{"rendered":"\n<p>Optical Character Recognition (OCR) is an important technology in computer vision and pattern recognition that recognizes text embedded in images. Although the OCR achieved high accuracy for languages with alphabet-based writing systems, its performance on handwritten Chinese text is poor due to the complexity of the Chinese writing system. In order to improve the accuracy rate, this paper proposes an integrated OCR model for Chinese handwriting that combines existing methods in the pre-processing phase, recognition phase, and post-processing phase.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Optical Character Recognition (OCR) is an important technology in computer vision and pattern recognition that recognizes text embedded in images. Although the OCR achieved high accuracy for languages with alphabet-based writing systems, its performance on handwritten Chinese text is poor &hellip; <a href=\"https:\/\/portfolios.cs.earlham.edu\/index.php\/2020\/10\/11\/cs488-abstract\/\">Read More<\/a><\/p>\n","protected":false},"author":90,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[101,119,19],"tags":[],"class_list":["post-4093","post","type-post","status-publish","format-standard","hentry","category-101","category-kate-nguyen","category-student"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>CS488 - Abstract - CS\/DS Student Portfolios<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/portfolios.cs.earlham.edu\/index.php\/2020\/10\/11\/cs488-abstract\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"CS488 - Abstract - CS\/DS Student Portfolios\" \/>\n<meta property=\"og:description\" content=\"Optical Character Recognition (OCR) is an important technology in computer vision and pattern recognition that recognizes text embedded in images. 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Although the OCR achieved high accuracy for languages with alphabet-based writing systems, its performance on handwritten Chinese text is poor &hellip; Read More","_links":{"self":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts\/4093","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/users\/90"}],"replies":[{"embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/comments?post=4093"}],"version-history":[{"count":1,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts\/4093\/revisions"}],"predecessor-version":[{"id":4094,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts\/4093\/revisions\/4094"}],"wp:attachment":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/media?parent=4093"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/categories?post=4093"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/tags?post=4093"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}