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@inproceedings{2233379,
author = {Geletka, Martin and Bankovič, Mikuláš and Meluš, Dávid and Ščavnická, Šárka and Štefánik, Michal and Sojka, Petr},
address = {Brno},
booktitle = {Recent Advances in Slavonic Natural Language Processing (RASLAN 2022)},
editor = {Aleš Horák, Pavel Rychlý, Adam Rambousek},
keywords = {OCR; Multi-modal learning; Information extraction; Transformers; Structured Documents},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Brno},
isbn = {978-80-263-1752-4},
pages = {35-46},
publisher = {Tribun EU},
title = {Information Extraction from Business Documents},
url = {https://nlp.fi.muni.cz/raslan/2022/paper18.pdf},
year = {2022}
}
TY - JOUR
ID - 2233379
AU - Geletka, Martin - Bankovič, Mikuláš - Meluš, Dávid - Ščavnická, Šárka - Štefánik, Michal - Sojka, Petr
PY - 2022
TI - Information Extraction from Business Documents
PB - Tribun EU
CY - Brno
SN - 9788026317524
KW - OCR
KW - Multi-modal learning
KW - Information extraction
KW - Transformers
KW - Structured Documents
UR - https://nlp.fi.muni.cz/raslan/2022/paper18.pdf
N2 - Document AI is a relatively new research topic that refers to techniques for automatically reading, understanding, and analyzing business documents. Nowadays, many companies extract data from business documents through manual efforts that are time-consuming and expensive, requiring manual customization or configuration. This paper describes techniques to address these problems, apply them to real-world data, and implement them to an end-to-end solution for automatic information extraction from business documents.
ER -
GELETKA, Martin, Mikuláš BANKOVIČ, Dávid MELUŠ, Šárka ŠČAVNICKÁ, Michal ŠTEFÁNIK and Petr SOJKA. Information Extraction from Business Documents. In Aleš Horák, Pavel Rychlý, Adam Rambousek. \textit{Recent Advances in Slavonic Natural Language Processing (RASLAN 2022)}. Brno: Tribun EU, 2022, p.~35-46. ISBN~978-80-263-1752-4.
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