Object Detection Pipeline Using YOLOv8 for Document Information Extraction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969624" target="_blank" >RIV/49777513:23520/23:43969624 - isvavai.cz</a>

Výsledek na webu

<a href="https://ceur-ws.org/Vol-3497/paper-051.pdf" target="_blank" >https://ceur-ws.org/Vol-3497/paper-051.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Object Detection Pipeline Using YOLOv8 for Document Information Extraction

Popis výsledku v původním jazyce

The extraction of information from semi-structured documents is an ongoing problem. This task is often approached from the perspective of NLP and large transformer-based models are employed. In our work, we successfully demonstrated that the Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR) tasks can be effectively addressed as object detection problems using a convolutional neural network (CNN) model. We utilized a relatively small and fast YOLOv8 model for which we conducted a series of experiments to explore the impact of different factors on model training. With YOLOv8, we were able to achieve AP 0.716 on the KILE task and 0.638 on the LIR task. Our code is available at https://github.com/strakaj/YOLOv8-for-document-understanding.git.

Název v anglickém jazyce

Object Detection Pipeline Using YOLOv8 for Document Information Extraction

Popis výsledku anglicky

The extraction of information from semi-structured documents is an ongoing problem. This task is often approached from the perspective of NLP and large transformer-based models are employed. In our work, we successfully demonstrated that the Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR) tasks can be effectively addressed as object detection problems using a convolutional neural network (CNN) model. We utilized a relatively small and fast YOLOv8 model for which we conducted a series of experiments to explore the impact of different factors on model training. With YOLOv8, we were able to achieve AP 0.716 on the KILE task and 0.638 on the LIR task. Our code is available at https://github.com/strakaj/YOLOv8-for-document-understanding.git.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

CEUR Workshop Proceedings

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

15

Strana od-do

583-597

Název nakladatele

CEUR-WS

Místo vydání

Thessaloniki

Místo konání akce

Thessaloniki, Greece

Datum konání akce

18. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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