In this paper, we target at the problem of sketch recognition.
\nWe systematically study how to incorporate users\u2019 correction
\nand editing into isolated and full sketch recognition. This is a
\nnatural and necessary interaction in real systems such as Visio
\nwhere very similar shapes exist. First, a novel algorithm
\nis proposed to mine the prior shape knowledge for three editing
\nmodes. Second, to differentiate visually similar shapes, a
\nnovel symbol recognition algorithm is introduced by leveraging
\nthe learnt shape knowledge. Then, a novel editing detection
\nalgorithm is proposed to facilitate symbol recognition.
\nFurthermore, both of the symbol recognizer and the editing
\ndetector are systematically incorporated into the full sketch
\nrecognition. Finally, based on the proposed algorithms, a realtime
\nsketch recognition system is built to recognize handdrawn
\nflowcharts and diagrams with flexible interactions. Extensive
\nexperiments show the effectiveness of the proposed
\nalgorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we target at the problem of sketch recognition. We systematically study how to incorporate users\u2019 correction and editing into isolated and full sketch recognition. This is a natural and necessary interaction in real systems such as Visio where very similar shapes exist. First, a novel algorithm is proposed to mine the prior […]<\/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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13562],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-267810","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2014-07-29","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"267813","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"08-2014-aaai-Wu-v0.3-final","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/07\/08-2014-aaai-Wu-v0.3-final.pdf","id":267813,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Jie Wu","user_id":0,"rest_url":false},{"type":"user_nicename","value":"chw","user_id":31440,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=chw"},{"type":"text","value":"Liqing Zhang","user_id":0,"rest_url":false},{"type":"user_nicename","value":"yongrui","user_id":35040,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=yongrui"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[171319],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":171319,"post_title":"Sketch Recognition","post_name":"sketch-recognition","post_type":"msr-project","post_date":"2015-08-01 00:18:41","post_modified":"2017-06-16 12:55:53","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/sketch-recognition\/","post_excerpt":"We built the Sketch2Tag system for hand-drawn sketch recognition. Due to large variations presented in hand-drawn sketches, most of existing work was limited to a particular domain or limited pre-defined classes. Different from existing work, Sketch2Tag is a general sketch recognition system, towards recognizing any semantically meaningful object that a child can recognize. This system enables a user to draw a sketch on the query panel, and then provides real-time recognition results. 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