dc.contributor.advisor | Røise, Tom | |
dc.contributor.author | Arnkværn, Brage | |
dc.contributor.author | Schoeler, Sigurd | |
dc.date.accessioned | 2021-09-15T16:33:07Z | |
dc.date.available | 2021-09-15T16:33:07Z | |
dc.date.issued | 2021 | |
dc.identifier | no.ntnu:inspera:77257183:82670321 | |
dc.identifier.uri | https://hdl.handle.net/11250/2777965 | |
dc.description.abstract | Denne oppgaven dokumenterer prosessen vår med å få en maskinlæringsmodell til å forstå finansielle dokumenter og tolke dem til JSON. Vi opprettet et datasett og evaluerte flere modeller, men endte opp med YoloV3 modellen for å finne feltene. Den endelige modellen finner riktige felter 57.56% ganger på testsettet vårt. | |
dc.description.abstract | This thesis documents our process of getting a machine learning model to un- derstand financial documents and parse them to JSON. We created a dataset and evaluated multiple models, but ended up with YoloV3 to locate the fields. The final model finds the correct fields 57.56% of times on our test set. | |
dc.language | eng | |
dc.publisher | NTNU | |
dc.title | FinanceDoc2JSON: Parsing and structuring invoices and other financial documents with deep learning | |
dc.type | Bachelor thesis | |