Symbol spotting for architectural drawings: state-of-the-art and new industry-driven developments

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dc.contributor.authorRezvanifar, Alireza
dc.contributor.authorCote, Melissa
dc.contributor.authorAlbu, Alexandra Branzan
dc.date.accessioned2020-11-30T22:38:26Z
dc.date.available2020-11-30T22:38:26Z
dc.date.copyright2019en_US
dc.date.issued2019
dc.description.abstractThis review paper offers a contemporary literature survey on symbol spotting in architectural drawing images. Research on isolated symbol recognition is quite mature; the same cannot be said for recognizing a symbol in context. One important challenge is the segmentation/recognition paradox: a system should segment symbols before recognizing them, but some kind of recognition may be necessary to obtain a correct segmentation. Research has thus been recently directed toward symbol spotting, a way of locating possible symbol instances without using full recognition methods. In this paper, we thoroughly review symbol spotting methods with a focus on architectural drawings, an application domain providing the document image analysis and graphic recognition communities with an interesting set of challenges linked to the sheer complexity and density of embedded information, that have yet to be resolved. While most existing methods perform well in terms of recall, their performance is rather poor in terms of precision and false positives. In light of the review, we also propose a simple yet effective symbol spotting method based on template matching and a novel clutter-tolerant cross-correlation function that achieves state-of-the-art results with high precision, high recall, and few false positives, able to cope with “real-life clutter” found in industry-standard architectural drawings.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipAcknowledgements This research was enabled in part by support provided by WestGrid (www. westgrid.ca) and Compute Canada Calcul Canada (www.computecanada.ca), as well as by the Natural Sciences and Engineering Research Council of Canada and Triumph Electrical Consulting Engineering Ltd. through the Collaborative Research and Development Grants Program. The authors would like to thank Steven Cooke at Triumph for his insights on how to interpret architectural drawings. Funding This research was supported by the Natural Sciences and Engineering Research Council of Canada and Triumph Electrical Consulting Engineering Ltd. through the Collaborative Research and Development Grants program. The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.en_US
dc.identifier.citationRezvanifar, A., Cote, M., & Albu, A. B. (2019). Symbol spotting for architectural drawings: state-of-the-art and new industry-driven developments. IPSJ Transactions on Computer Vision and Applications, 11(1). https://doi.org/10.1186/s41074-019-0055-1en_US
dc.identifier.urihttps://doi.org/10.1186/s41074-019-0055-1
dc.identifier.urihttp://hdl.handle.net/1828/12415
dc.language.isoenen_US
dc.publisherIPSJ Transactions on Computer Vision and Applicationsen_US
dc.subject.departmentDepartment of Electrical and Computer Engineering
dc.titleSymbol spotting for architectural drawings: state-of-the-art and new industry-driven developmentsen_US
dc.typeArticleen_US

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