Upotreba dubokog učenja za klasifikaciju tehničkih dokumenat
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| Názov: | Upotreba dubokog učenja za klasifikaciju tehničkih dokumenat |
|---|---|
| Autori: | Kokanović, Karlo |
| Prispievatelia: | Herceg, Marijan |
| Informácie o vydavateľovi: | Sveučilište Josipa Jurja Strossmayera u Osijeku. Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek. Zavod za komunikacije. Katedra za elektroniku i mikroelektroniku., 2025. |
| Rok vydania: | 2025 |
| Predmety: | classification, TECHNICAL SCIENCES. Electrical Engineering. Telecommunications and Informatics, tehnička dokumentacija, deep learning, TEHNIČKE ZNANOSTI. Elektrotehnika. Telekomunikacije i informatika, duboko učenje, GraphSAGE, klasifikacija, technical documentation, Python |
| Popis: | As part of this work, a deep neural network model was developed for the classification of technical documentation. For classification of technical documentation, a model was developed that refers to documents of the electricity section within the IPC categorization. For training purposes, a database of 6000 unique documents from six different classes within electricity (H01, H02, H03, H04, H05 and H10) was first collected. Data within each document was extracted using the tesseract algorithm, where both textual and image elements were extracted. The document title and its summary were used from the textual part, and the diagram located on the first page of the document was used for the image part. This information is pre-processed to match the format of the final model. Since the images within the documents differ, the YOLOV5 model was used to classify the images into electronic diagrams or flowcharts. If an image is an electronic schematic, the VGG19 model is applied to it to handle and extract relevant features. If the image is a flowchart, the textual content of that flowchart is extracted and preprocessed. The GraphSAGE neural network graph model was used for document classification. The final deep neural network model achieved significant results during the training, validation and testing process. The model was shown to have consistent prediction with minimal deviations in classification metrics and to be able to correctly handle features within technical documentation. U sklopu ovog rada razvijen je model duboke neuronske mreže za klasifikaciju tehničke dokumentacije. Tehnička dokumentacija, za čiju je klasifikaciju model razvijen, se odnosi na dokumente odjeljka elektricitet unutar IPC kategorizacije. Za potrebe treniranja najprije je prikupljena baza podataka od 6000 unikatnih dokumenata iz šest različitih klasa unutar elektriciteta (H01, H02, H03, H04, H05 i H10). Podatci unutar svakog dokumenta izvlačeni su putem tesseract algoritma, pri čemu su izdvojeni i tekstualni i slikovni elementi. Od tekstualnog dijela korišteni su naslov dokumenta i njegov sažetak, a za slikovni dio korištena je shema koja se nalazi na prvoj stranici dokumenta. Te su informacije predobrađene kako bi odgovarale formatu konačnog modela. Budući da se slike unutar dokumenata razlikuju, korišten je model YOLOV5 za klasifikaciju slika na elektroničke sheme ili dijagrame tijeka. Ako je neka slika elektronička shema, na nju je primijenjen VGG19 model za rukovanje i izvlačenje relevantnih značajki. Ako je pak slika dijagram tijeka, izvlači se tekstualni sadržaj tog dijagrama i predobrađuje. Za klasifikaciju dokumenata korišten je model graf neuronskih mreža GraphSAGE. Konačni model duboke neuronske mreže postigao je značajne rezultate prilikom procesa treniranja, validacije i testiranja. Pokazano je da model ima konzistentnu predikciju uz minimalna odstupanja metrika klasifikacije te da može pravilno rukovati značajkama unutar tehničke dokumentacije. |
| Druh dokumentu: | Master thesis |
| Popis súboru: | application/pdf |
| Jazyk: | Croatian |
| Prístupová URL adresa: | https://repozitorij.etfos.hr/islandora/object/etfos:5513/datastream/PDF https://urn.nsk.hr/urn:nbn:hr:200:354014 https://repozitorij.etfos.hr/islandora/object/etfos:5513 |
| Rights: | URL: http://rightsstatements.org/vocab/InC/1.0/ |
| Prístupové číslo: | edsair.od......3912..67c4bd32d6ac7891bd06a1ac6d03736d |
| Databáza: | OpenAIRE |
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| Header | DbId: edsair DbLabel: OpenAIRE An: edsair.od......3912..67c4bd32d6ac7891bd06a1ac6d03736d RelevancyScore: 887 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 886.749633789063 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Upotreba dubokog učenja za klasifikaciju tehničkih dokumenat – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kokanović%2C+Karlo%22">Kokanović, Karlo</searchLink> – Name: Author Label: Contributors Group: Au Data: Herceg, Marijan – Name: Publisher Label: Publisher Information Group: PubInfo Data: Sveučilište Josipa Jurja Strossmayera u Osijeku. Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek. Zavod za komunikacije. Katedra za elektroniku i mikroelektroniku., 2025. – Name: DatePubCY Label: Publication Year Group: Date Data: 2025 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22classification%22">classification</searchLink><br /><searchLink fieldCode="DE" term="%22TECHNICAL+SCIENCES%2E+Electrical+Engineering%2E+Telecommunications+and+Informatics%22">TECHNICAL SCIENCES. Electrical Engineering. Telecommunications and Informatics</searchLink><br /><searchLink fieldCode="DE" term="%22tehnička+dokumentacija%22">tehnička dokumentacija</searchLink><br /><searchLink fieldCode="DE" term="%22deep+learning%22">deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22TEHNIČKE+ZNANOSTI%2E+Elektrotehnika%2E+Telekomunikacije+i+informatika%22">TEHNIČKE ZNANOSTI. Elektrotehnika. Telekomunikacije i informatika</searchLink><br /><searchLink fieldCode="DE" term="%22duboko+učenje%22">duboko učenje</searchLink><br /><searchLink fieldCode="DE" term="%22GraphSAGE%22">GraphSAGE</searchLink><br /><searchLink fieldCode="DE" term="%22klasifikacija%22">klasifikacija</searchLink><br /><searchLink fieldCode="DE" term="%22technical+documentation%22">technical documentation</searchLink><br /><searchLink fieldCode="DE" term="%22Python%22">Python</searchLink> – Name: Abstract Label: Description Group: Ab Data: As part of this work, a deep neural network model was developed for the classification of technical documentation. For classification of technical documentation, a model was developed that refers to documents of the electricity section within the IPC categorization. For training purposes, a database of 6000 unique documents from six different classes within electricity (H01, H02, H03, H04, H05 and H10) was first collected. Data within each document was extracted using the tesseract algorithm, where both textual and image elements were extracted. The document title and its summary were used from the textual part, and the diagram located on the first page of the document was used for the image part. This information is pre-processed to match the format of the final model. Since the images within the documents differ, the YOLOV5 model was used to classify the images into electronic diagrams or flowcharts. If an image is an electronic schematic, the VGG19 model is applied to it to handle and extract relevant features. If the image is a flowchart, the textual content of that flowchart is extracted and preprocessed. The GraphSAGE neural network graph model was used for document classification. The final deep neural network model achieved significant results during the training, validation and testing process. The model was shown to have consistent prediction with minimal deviations in classification metrics and to be able to correctly handle features within technical documentation.<br />U sklopu ovog rada razvijen je model duboke neuronske mreže za klasifikaciju tehničke dokumentacije. Tehnička dokumentacija, za čiju je klasifikaciju model razvijen, se odnosi na dokumente odjeljka elektricitet unutar IPC kategorizacije. Za potrebe treniranja najprije je prikupljena baza podataka od 6000 unikatnih dokumenata iz šest različitih klasa unutar elektriciteta (H01, H02, H03, H04, H05 i H10). Podatci unutar svakog dokumenta izvlačeni su putem tesseract algoritma, pri čemu su izdvojeni i tekstualni i slikovni elementi. Od tekstualnog dijela korišteni su naslov dokumenta i njegov sažetak, a za slikovni dio korištena je shema koja se nalazi na prvoj stranici dokumenta. Te su informacije predobrađene kako bi odgovarale formatu konačnog modela. Budući da se slike unutar dokumenata razlikuju, korišten je model YOLOV5 za klasifikaciju slika na elektroničke sheme ili dijagrame tijeka. Ako je neka slika elektronička shema, na nju je primijenjen VGG19 model za rukovanje i izvlačenje relevantnih značajki. Ako je pak slika dijagram tijeka, izvlači se tekstualni sadržaj tog dijagrama i predobrađuje. Za klasifikaciju dokumenata korišten je model graf neuronskih mreža GraphSAGE. Konačni model duboke neuronske mreže postigao je značajne rezultate prilikom procesa treniranja, validacije i testiranja. Pokazano je da model ima konzistentnu predikciju uz minimalna odstupanja metrika klasifikacije te da može pravilno rukovati značajkama unutar tehničke dokumentacije. – Name: TypeDocument Label: Document Type Group: TypDoc Data: Master thesis – Name: Format Label: File Description Group: SrcInfo Data: application/pdf – Name: Language Label: Language Group: Lang Data: Croatian – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://repozitorij.etfos.hr/islandora/object/etfos:5513/datastream/PDF" linkWindow="_blank">https://repozitorij.etfos.hr/islandora/object/etfos:5513/datastream/PDF</link><br /><link linkTarget="URL" linkTerm="https://urn.nsk.hr/urn:nbn:hr:200:354014" linkWindow="_blank">https://urn.nsk.hr/urn:nbn:hr:200:354014</link><br /><link linkTarget="URL" linkTerm="https://repozitorij.etfos.hr/islandora/object/etfos:5513" linkWindow="_blank">https://repozitorij.etfos.hr/islandora/object/etfos:5513</link> – Name: Copyright Label: Rights Group: Cpyrght Data: URL: http://rightsstatements.org/vocab/InC/1.0/ – Name: AN Label: Accession Number Group: ID Data: edsair.od......3912..67c4bd32d6ac7891bd06a1ac6d03736d |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: Croatian Subjects: – SubjectFull: classification Type: general – SubjectFull: TECHNICAL SCIENCES. Electrical Engineering. Telecommunications and Informatics Type: general – SubjectFull: tehnička dokumentacija Type: general – SubjectFull: deep learning Type: general – SubjectFull: TEHNIČKE ZNANOSTI. Elektrotehnika. Telekomunikacije i informatika Type: general – SubjectFull: duboko učenje Type: general – SubjectFull: GraphSAGE Type: general – SubjectFull: klasifikacija Type: general – SubjectFull: technical documentation Type: general – SubjectFull: Python Type: general Titles: – TitleFull: Upotreba dubokog učenja za klasifikaciju tehničkih dokumenat Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kokanović, Karlo – PersonEntity: Name: NameFull: Herceg, Marijan IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 02 Type: published Y: 2025 Identifiers: – Type: issn-locals Value: edsair |
| ResultId | 1 |