{"id":155998,"date":"2001-01-01T00:00:00","date_gmt":"2001-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/turbo-recognition-a-statistical-approach-to-layout-analysis\/"},"modified":"2018-10-16T20:12:58","modified_gmt":"2018-10-17T03:12:58","slug":"turbo-recognition-a-statistical-approach-to-layout-analysis","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/turbo-recognition-a-statistical-approach-to-layout-analysis\/","title":{"rendered":"Turbo recognition: a statistical approach to layout analysis"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Turbo recognition (TR) is a communication theory approach to the analysis of rectangular layouts, in the spirit<br \/>\nof Document Image Decoding. The TR algorithm, inspired by turbo decoding, is based on a generative model of<br \/>\nimage production, in which two grammars are used simultaneously to describe structure in orthogonal (horizontal<br \/>\nand vertical) directions. This enables TR to strictly embody non-local constraints that cannot be taken into<br \/>\naccount by local statistical methods. This basis in finite state grammars also allows TR to be quickly retargetable<br \/>\nto new domains. We illustrate some of the capabilities of TR with two examples involving realistic images. While<br \/>\nTR, like turbo decoding, is not guaranteed to recover the statistically optimal solution, we present an experiment<br \/>\nthat demonstrates its ability to produce optimal or near-optimal results on a simple yet nontrivial example, the<br \/>\nrecovery of a filled rectangle in the midst of noise. Unlike methods such as stochastic context free grammars and<br \/>\nexhaustive search, which are often intractable beyond small images, turbo recognition scales linearly with image<br \/>\nsize, suggesting TR as an e\u00c6cient yet near-optimal approach to statistical layout analysis.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Turbo recognition (TR) is a communication theory approach to the analysis of rectangular layouts, in the spirit of Document Image Decoding. The TR algorithm, inspired by turbo decoding, is based on a generative model of image production, in which two grammars are used simultaneously to describe structure in orthogonal (horizontal and vertical) directions. This enables [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Electronic Imaging Conf. on Document Recognition and Retrieval","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Electronic Imaging Conf. on Document Recognition and Retrieval","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Taku A. Tokuyasu, Philip A. 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