Extracting Decision Models from Digitally Drawn or Hand-drawn DMN Images

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dc.contributor.authorLeribaux, Aurélie
dc.contributor.authorHeijmans, Caroline
dc.contributor.authorGoossens, Alexandre
dc.contributor.authorVanthienen, Jan
dc.date.accessioned2024-12-26T21:09:24Z
dc.date.available2024-12-26T21:09:24Z
dc.date.issued2025-01-07
dc.description.abstractDecision Model and Notation (DMN) models are used to model and automate operational decisions. Frequently, these DMN models are distributed as images within documents, either as screenshots or as pictures of hand-drawn models. This distribution method can results in the loss of the original source format. Re-using these images then entails the manual process of remodelling or redrawing them, a task that is both time-consuming and complex. In this study, deep learning techniques are employed to extract DMN models from both digitally drawn and hand-drawn DMN images. A substantial dataset was collected and annotated to train and test the diverse range of models. Subsequently, the work's outcome has been integrated into a DMN Computer Vision Tool application which can be used to reconstruct DMN source files based on hand-drawn sketches and digital images.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.679
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other13615b8c-19ed-4338-a885-735cf3b5643b
dc.identifier.urihttps://hdl.handle.net/10125/109527
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBusiness Process Technology
dc.subjectcomputer vision, deep learning, dmn
dc.titleExtracting Decision Models from Digitally Drawn or Hand-drawn DMN Images
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage5668

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