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Title:Analiza uspešnosti optične prepoznave elementov BPMN : magistrsko delo
Authors:Jagečić, Slavica (Author)
Polančič, Gregor (Mentor) More about this mentor... New window
Files:.pdf MAG_Jagecic_Slavica_2019.pdf (4,06 MB)
MD5: AD972EFEFD0C65B420DE91B216494668
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrsko delo predstavlja postopek izdelave modela za prepoznavo ročno risanih BPMN elementov ter pridobitev rezultatov (%) uspešnosti njihove prepoznave. Za pomoč pri razvoju modela za prepoznavo elementov BPMN smo uporabili ogrodje TensorFlow. Opravili smo pregled literature, predstavili obstoječe rešitve, razvite na podlagi optične prepoznave in strojnega učenja. Razložili smo osnovne gradnike BPMN (standard BPMN 2.0.) in nekatere od teh elementov vključili v proces analize uspešnosti razpoznave s pomočjo mobilne aplikacije, izdelane v okviru naloge in razvite v okolju Angular.js, v katero smo vključili izdelani TensorFlow model, ki je zmožen prepoznavati BPMN elemente. V analizi smo zapisali ugotovitve, ki smo jih pridobili v raziskovalnemu delu na podlagi vprašalnikov. Ugotovitve, pridobljene v analizi, so pokazale da je mobilna aplikacija zmožna prepoznavati določene elemente BPMN, vendar ne vseh. Prav tako smo podali smernice za nadaljnje delo.
Keywords:BPMN, OCR, strojno učenje, TensorFlow
Year of publishing:2019
Place of performance:Maribor
Publisher:[S. Jagečić]
Number of pages:XI, 96 str.
Source:Maribor
UDC:004.424.3(043.2)
COBISS_ID:22839574 New window
NUK URN:URN:SI:UM:DK:YZIIQ6KK
Views:291
Downloads:47
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:30.08.2019

Secondary language

Language:English
Title:Analysis of the effectiveness of optical recognition of BPMN elements
Abstract:The master thesis represents the process of creating a model capable of recognizing handwritten BPMN elements and receiving the results with percent precision (%) of recognition success. To create a model for identifying BPMN elements, we used the TensorFlow framework. We made a literature review, explored existing solutions for optical recognition and machine learning. We also explained the basic BPMN (BPMN 2.0. specification) elements and used some of those elements in the process of analyzing the recognition performance with the use of a mobile application, developed with Angular.js framework. In this application, we incorporated the TensorFlow model capable of detecting BPMN elements. In the analysis, we recorded the findings obtained in the research work based on a survey. The findings obtained in the analysis showed that the mobile application is capable of identifying certain BPMN elements, but not all. We also gave guidelines for further work.
Keywords:BPMN, OCR, machine learning, TensorFlow


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