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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 a 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, s.~35-46. ISBN~978-80-263-1752-4.
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