{"id":2417,"date":"2026-07-16T04:00:00","date_gmt":"2026-07-16T04:00:00","guid":{"rendered":"https:\/\/futurenews24.com\/index.php\/2026\/07\/16\/2607-13639\/"},"modified":"2026-07-16T10:59:30","modified_gmt":"2026-07-16T10:59:30","slug":"2607-13639","status":"publish","type":"post","link":"https:\/\/futurenews24.com\/index.php\/2026\/07\/16\/2607-13639\/","title":{"rendered":"OvisOCR2 Technical Report"},"content":{"rendered":"<p><br \/>\n<br \/>arXiv:2607.13639v1 Announce Kind: cross<br \/>\nSummary: We introduce OvisOCR2, a 0.8B doc parsing mannequin. OvisOCR2 is designed as an end-to-end parser: given a doc web page picture, it generates a Markdown illustration in pure studying order, overlaying textual content, formulation, tables, and visible areas. We construct a knowledge engine that mixes filtered real-document annotations with artificial pages whose rendered photos and Markdown targets are derived from the identical HTML supply. The coaching recipe consists of supervised fine-tuning, reinforcement studying on a 4B department with a multi-component reward design, on-policy distillation into the 0.8B mannequin, and mannequin fusion. On OmniDocBench v1.6, OvisOCR2 achieves a state-of-the-art total rating of 96.58, inserting an end-to-end mannequin on the high of this leaderboard beforehand dominated by pipeline strategies and highlighting the potential of end-to-end doc parsing. On PureDocBench, OvisOCR2 additionally achieves the best Avg3 rating of 75.06. Past these two public benchmarks, we consider OvisOCR2 on an in-house benchmark designed to cowl a broader set of long-tail and difficult eventualities. OvisOCR2 obtains the most effective total efficiency among the many in contrast strategies, offering additional proof of its generalization and robustness. OvisOCR2 is obtainable at https:\/\/huggingface.co\/ATH-MaaS\/OvisOCR2.<br \/>\n<br \/><br \/>\n<br \/><a href=\"https:\/\/arxiv.org\/abs\/2607.13639\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>arXiv:2607.13639v1 Announce Kind: cross Summary: We introduce OvisOCR2, a 0.8B doc parsing mannequin. OvisOCR2 is designed as an end-to-end parser: given a doc web page picture, it generates a Markdown illustration in pure studying order, overlaying textual content, formulation, tables, and visible areas. We construct a knowledge engine that mixes filtered real-document annotations with artificial [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2419,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"http:\/\/arxiv.org\/static\/browse\/0.3.4\/images\/arxiv-logo-fb.png","fifu_image_alt":"","jnews-multi-image_gallery":[],"jnews_single_post":[],"jnews_primary_category":[],"jnews_override_bookmark_settings":[],"jnews_social_meta":[],"jnews_override_counter":[],"footnotes":""},"categories":[2],"tags":[2906,600,2907],"class_list":["post-2417","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-research-breakthroughs","tag-ovisocr2","tag-report","tag-technical"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>OvisOCR2 Technical Report - Future News 24<\/title>\n<meta name=\"description\" content=\"Abstract page for arXiv paper 2607.13639: OvisOCR2 Technical Report\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/futurenews24.com\/index.php\/2026\/07\/16\/2607-13639\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"OvisOCR2 Technical Report - 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