143
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Information extraction for different layouts of invoice images

& ORCID Icon
Pages 417-429 | Received 12 Sep 2022, Accepted 05 Dec 2022, Published online: 03 Mar 2023

References

  • Hamza H, Belaid Y, Belaid A. A case-based reasoning approach for invoice structure extraction. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007); Curitiba (Brazil): IEEE; 2007. p. 327–331.
  • Peng H, Long F, Chi Z. Document image recognition based on template matching of component block projections. IEEE Trans Pattern Anal Mach Intell. 2003;25:1188–1192. https://doi.org/10.1109/TPAMI.2003.1227996.
  • Schuster D, Muthmann K, Esser D, et al. Intellix – end-user trained information extraction for document archiving. In: 2013 12th International Conference on Document Analysis and Recognition; Washington (DC, USA); IEEE; 2013. p. 101–105.
  • Sun Y, Mao X, Hong S, et al. Template matching-based method for intelligent invoice information identification. IEEE Access. 2019;7:28392–28401. https://doi.org/10.1109/ACCESS.2019.2901943.
  • Chalkidis I, Androutsopoulos I. A deep learning approach to contract element extraction Legal knowledge and information systems. Frontiers in Artificial Intelligence and Applications. Vol. 302. IOS Press; 2017. p. 155–164. https://doi.org/10.3233/978-1-61499-838-9-155.
  • Batbaatar E, Ryu KH. Ontology-based healthcare named entity recognition from twitter messages using a recurrent neural network approach. Int J Environ Res Public Health. 2019;16(19): 3628. https://doi.org/10.3390/ijerph16193628.
  • Liu X, Gao F, Zhang Q, et al. Graph convolution for multimodal information extraction from visually rich documents. CoRR abs/1903.11279. 2019.
  • Yu W, Lu N, Qi X, et al. PICK: processing key information extraction from documents using improved graph learning-convolutional networks. In: 2020 25th International Conference on Pattern Recognition (ICPR); Milan (Italy): IEEE; 2021. p. 4363–4370.
  • Xu Y, Xu Y, Lv T, et al. LayoutLMv2: multi-modal pre-training for visually-rich document understanding. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL); 2021; Bangkok, (Thailand): Association for Computational Linguistics; 2020.
  • Li Y, Qian Y, Yu Y, et al. StrucTexT: structured text understanding with multi-modal transformers. In: Proceedings of the 29th ACM International Conference on Multimedia; Virtual event China: ACM; 2021. p. 1912–1920.
  • Leon D. Extracting information from PDF invoices using deep learning. KTH, School of Electrical Engineering; 2021.
  • Francis S, Van Landeghem J, Moens M-F. Transfer learning for named entity recognition in financial and biomedical documents. Information. 2019;10(8):248. https://doi.org/10.3390/info10080248.
  • Liu X, Hersch G L, Khalil I. Clinical trial information extraction with BERT. In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI); Victoria (BC, Canada): IEEE; 2021. p. 505–506.
  • Dias M, Boné J, Ferreira JC, et al. Named entity recognition for sensitive data discovery in Portuguese. Appl Sci. 2020;10(7): 2303. https://doi.org/10.3390/app10072303.
  • Baviskar D, Ahirrao S, Kotecha K. Multi-layout unstructured invoice documents dataset: a dataset for template-free invoice processing and its evaluation using AI approaches. IEEE Access. 2021;9:101494–101512. https://doi.org/10.1109/ACCESS.2021.3096739.
  • Polpanumas C, Phatthiyaphaibun W. thai2fit: Thai language Implementation of ULMFit. Zenodo; 2021.
  • cstorm125, lukkiddd: PyThaiNLP/classification-benchmarks: v0.1-alpha. Zenodo; 2020.
  • Segura-Bedmar I, Martinez P, Herrero-Zazo M. SemEval-2013 Task 9: Extraction of drug-drug interactions from biomedical texts (DDIExtraction 2013). In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Vol. 2., Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013); Atlanta, Georgia, USA: Association for Computational Linguistics; 2013. p. 341–350.
  • Chinchor N, Sundheim B. MUC-5 evaluation metrics. In: Fifth Message Understanding Conference (MUC-5): Proceedings of a Conference Held in Baltimore; Maryland: Aug 25–27; ACL Anthology; 1993. https://aclanthology.org/M93-1000.
  • Li J, Chiu B, Feng S, et al. Few-shot named entity recognition via meta-learning. IEEE Trans Knowl Data Eng. 2022;34:4245–4256. https://doi.org/10.1109/TKDE.2020.3038670.
  • Li J, Han P, Ren X, et al. Sequence labeling with meta-learning. IEEE Trans Knowl Data Eng. 2021;1–1. https://doi.org/10.1109/TKDE.2021.3118469. https://ieeexplore.ieee.org/document/9563226.
  • Li J, Shang S, Chen L. Domain generalization for named entity boundary detection via metalearning. IEEE Trans Neural Netw Learn Syst. 2021;32:3819–3830. https://doi.org/10.1109/TNNLS.2020.3015912.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.