{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/synthetically-trained-icon-proposals-for","title":"Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics","arxiv_id":"1807.10441","date":"2018-07-27","proceeding":null,"authors":["Spandan Madan","Zoya Bylinskii","Matthew Tancik","Adrià Recasens","Kimberli Zhong","Sami Alsheikh","Hanspeter Pfister","Aude Oliva","Fredo Durand"],"abstract":"Widely used in news, business, and educational media, infographics are\nhandcrafted to effectively communicate messages about complex and often\nabstract topics including `ways to conserve the environment' and `understanding\nthe financial crisis'. Composed of stylistically and semantically diverse\nvisual and textual elements, infographics pose new challenges for computer\nvision. While automatic text extraction works well on infographics, computer\nvision approaches trained on natural images fail to identify the stand-alone\nvisual elements in infographics, or `icons'. To bridge this representation gap,\nwe propose a synthetic data generation strategy: we augment background patches\nin infographics from our Visually29K dataset with Internet-scraped icons which\nwe use as training data for an icon proposal mechanism. On a test set of 1K\nannotated infographics, icons are located with 38% precision and 34% recall\n(the best model trained with natural images achieves 14% precision and 7%\nrecall). Combining our icon proposals with icon classification and text\nextraction, we present a multi-modal summarization application. Our application\ntakes an infographic as input and automatically produces text tags and visual\nhashtags that are textually and visually representative of the infographic's\ntopics respectively.","url_abs":"http://arxiv.org/abs/1807.10441v1","url_pdf":"http://arxiv.org/pdf/1807.10441v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"synthetically-trained-icon-proposals-for","repo_url":"https://github.com/cvzoya/visuallydata","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"synthetic-data-generation","task_name":"Synthetic Data Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.10441","atlas_url":"https://app.syntology.ai/?focus=1807.10441","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}