[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-649683-105":59,"doc-detail-649683-en":129},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":122,"head_meta":124,"extra_data":126,"updated_unix":128},105,"en","recognizing-architectural-objects-in-floor-plan-drawings-using-deep-learning-style-transfer-algorithms","RECOGNIZING ARCHITECTURAL OBJECTS IN FLOOR-PLAN DRAWINGS USING DEEP-LEARNING STYLE-TRANSFER ALGORITHMS","","This paper presents a deep-learning approach for recognizing floor plans by extracting essential architectural objects and unifying their visual styles. Traditional floor-plan interpretation is manual, expert-dependent, and difficult for computers because existing drawings use different symbol representations. The method converts preexisting plans into an integrated, comprehensible format via Conditional Generative Adversarial Networks (cGAN). Approximately 100 floor plans from a national dataset are used, with wall, door, and window objects selected as extraction targets, enabling consistent segmentation and improved automatic information retrieval.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/recognizing-architectural-objects-in-floor-plan-drawings-using-deep-learning-style-transfer-algorithms/649683/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/recognizing-architectural-objects-in-floor-plan-drawings-using-deep-learning-style-transfer-algorithms/649683.png","ImageObject",300,407,{"name":92,"@type":93},"adawds","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What problem does the paper address in floor-plan recognition?","Question",{"text":111,"@type":112},"Computers struggle to extract information from existing floor plans because the same objects are represented by different symbols and visual styles across drawings and design phases.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"What is the main technical strategy proposed?",{"text":116,"@type":112},"The approach uses Conditional Generative Adversarial Networks (cGAN) to transfer styles and transform preexisting plans into a consistent, more readable integrated format.",{"name":118,"@type":109,"acceptedAnswer":119},"Which objects are selected for extraction in the proposed method?",{"text":120,"@type":112},"Wall, door, and window objects are selected as the target entities to be extracted and used as training inputs for the style-transfer model.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},649683,1792803864,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":138,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":67,"update_tm":128,"read_time":142},3985747872983,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","RECOGNIZING ARCHITECTURAL OBJECTS IN FLOOR-PLAN DRAWINGS USING DEEP-LEARNING STYLE-TRANSFER ALGORITHMS  \nDAHNGYU CHO 1 , JINSUNG KIM2 , EUNSEO SHIN3 ,  \nJUNGSIK CHOI4 and JIN-KOOK LEE5  \n1,2,3,5 Department of Interior Architecture and Built Environment, Yonsei University, Seoul, Republic of Korea  \n1,2,[3](3 {wheks822|wlstjd1320|silverw0721}@gmail.com)[ {wheks822|wlstjd1320|silverw0721}@gmail.com](3 {wheks822|wlstjd1320|silverw0721}@gmail.com)  \n[5](5 leejinkook@yonsei.ac.kr)[ leejinkook@yonsei.ac.kr](5 leejinkook@yonsei.ac.kr)  \n4 Major in Architecture IT Convergence Engineering, Division of Smart Convergence Engineering, Hanyang University ERICA, Gyeonggi, Republic of Korea  \n[4](4jungsikchoi@hanyang.ac.kr)[jungsikchoi@hanyang.ac.kr](4jungsikchoi@hanyang.ac.kr)  \nAbstract. This paper describes an approach of recognizing floor plans by assorting essential objects of the plan using deep-learning based style transfer algorithms. Previously, the recognition of floor plans in the design and remodeling phase was labor-intensive, requiring expert-dependent and manual interpretation. For a computer to take in the imaged architectural plan information, the symbols in the plan must be understood. However, the computer has difficulty in extracting information directly from the preexisting plans due to the different conditions of the plans. The goal is to change the preexisting plans to an integrated format to improve the readability by transferring their style into a comprehensible way using Conditional Generative Adversarial Networks (cGAN) . About 100-floor plans were used for the dataset which was previously constructed by the Ministry of Land, Infrastructure, and Transport of Korea. The proposed approach has such two steps: (1) to define the important objects contained in the floor plan which needs to be extracted and (2) to use the defined objects as training input data for the cGAN style transfer model. In this paper, wall, door, and window objects were selected as the target for extraction. The preexisting floor plans would be segmented into each part, altered into a consistent format which would then contribute to automatically extracting information for further utilization.  \nKeywords. Architectural objects; floor plan recognition; deep-learning; style-transfer.  \n1. Introduction  \nArchitectural drawing is the set of meaningful information needed for designing, narrating, and executing a construction project. Recognizing the information contained in the traditional drawings, which exists as forms of papers, CAD files,  \nRE: Anthropocene, Proceedings of the 25 th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2020 , Volume 2, 717-725 . © 2020 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.  \n718 D. CHO ET AL.  \netc., will help future architects for better designs. The work is time-consuming and labor-intensive, making it more efficient if the computer, not a human, can automatically interpret the drawings. Traditional drawings, however, are in a form that only humans can understand, and thus, there must be some tasks preceded for the computer to understand. To figure out the preceding work, architectural drawings were manually analyzed, and this led to the conclusion that the main reason the computer could not understand the drawings was due to the difficulty of recognizing the various symbols corresponding to one represented object in the drawing. For example, wall objects mainly consist of a pair of lines, but in some drawings, they may be represented in a color-filled form. Therefore, in this paper, a new strategy will be suggested to integrate the various styles of drawings by unifying the symbols that represent a single object for the better understanding of architectural drawings by the computer.  \nFigure 1 . Outline of the study.  \n2. Background  \n2.1. ARCHITECTURAL DRAWINGS  \nArchitectural draw","cbCaiu5yz9goc7Rl","https://ap.wps.com/l/cbCaiu5yz9goc7Rl","pdf",1371504,"English","# Abstract\n# Keywords\n# 1. Introduction\n# 2. Background\n## 2.1 Architectural Drawings","[{\"question\":\"What problem does the paper address in floor-plan recognition?\",\"answer\":\"Computers struggle to extract information from existing floor plans because the same objects are represented by different symbols and visual styles across drawings and design phases.\"},{\"question\":\"What is the main technical strategy proposed?\",\"answer\":\"The approach uses Conditional Generative Adversarial Networks (cGAN) to transfer styles and transform preexisting plans into a consistent, more readable integrated format.\"},{\"question\":\"Which objects are selected for extraction in the proposed method?\",\"answer\":\"Wall, door, and window objects are selected as the target entities to be extracted and used as training inputs for the style-transfer model.\"}]","RECOGNIZING ARCHITECTURAL OBJECTS IN FLOOR-PLAN DRAWINGS USING DEEP-LEARNING STYLE-TRANSFER ALGORITHMS | PDF",23]