Who is Selling to Whom – Feature Evaluation for Multi-block Classification in Invoice Information Extraction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00123275" target="_blank" >RIV/00216224:14330/21:00123275 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-87802-3_23" target="_blank" >http://dx.doi.org/10.1007/978-3-030-87802-3_23</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-87802-3_23" target="_blank" >10.1007/978-3-030-87802-3_23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Who is Selling to Whom – Feature Evaluation for Multi-block Classification in Invoice Information Extraction

Popis výsledku v původním jazyce

The invoice information extraction task aims at unifying the automatized processing of invoices in structured forms and in the form of a scanned image. Recognizing the pieces of information where a specific value is identified with a keyword (such as the invoice date) is a relatively well-managed task. On the other hand, identification of multi-block information on the invoice, such as distinguishing the seller, buyer, and the delivery address, is much more challenging due to versatile invoice layouts. In this work, we present a new technique of feature extraction and classification to recognize the seller, buyer, and delivery address text blocks in scanned invoices based on a combination of complex layout and annotated text features. The method does not only consider the block positional features but also the relation between blocks and block contents at a higher level. The technique is implemented as a module of the OCRMiner system. We offer its detailed evaluation and error analysis with a dataset of more than five hundred Czech invoices reaching the overall macro average F1-score of 94%.

Název v anglickém jazyce

Who is Selling to Whom – Feature Evaluation for Multi-block Classification in Invoice Information Extraction

Popis výsledku anglicky

The invoice information extraction task aims at unifying the automatized processing of invoices in structured forms and in the form of a scanned image. Recognizing the pieces of information where a specific value is identified with a keyword (such as the invoice date) is a relatively well-managed task. On the other hand, identification of multi-block information on the invoice, such as distinguishing the seller, buyer, and the delivery address, is much more challenging due to versatile invoice layouts. In this work, we present a new technique of feature extraction and classification to recognize the seller, buyer, and delivery address text blocks in scanned invoices based on a combination of complex layout and annotated text features. The method does not only consider the block positional features but also the relation between blocks and block contents at a higher level. The technique is implemented as a module of the OCRMiner system. We offer its detailed evaluation and error analysis with a dataset of more than five hundred Czech invoices reaching the overall macro average F1-score of 94%.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LM2018101" target="_blank" >LM2018101: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

SPECOM 2021: 23rd International Conference on Speech and Computer

ISBN

9783030878016

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

12

Strana od-do

250-261

Název nakladatele

Springer

Místo vydání

St. Petersburg, Russia

Místo konání akce

St. Petersburg, Russia

Datum konání akce

1. 1. 2021

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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