Mobile QR Code QR CODE

REFERENCES

1 
Kerroumi M., Sayem O., Shabou A., 2021, VisualWordGrid: Information Extraction from Scanned Documents Using a Multimodal Approach, in Document Analysis and Recognition - ICDAR 2021 Workshops, Springer International Publishing, pp. 389-402DOI
2 
Katti A. R., et al. , 2018, Chargrid: Towards Understanding 2D Documents., arXivDOI
3 
Denk T. I., 2019, Wordgrid: Extending Chargrid with Word-level InformationDOI
4 
Joan S. P. F., Valli S., Jan. 2018, A Survey on Text Information Extraction from Born-Digital and Scene Text Images, Proc. Natl. Acad. Sci. India Sect. Phys. Sci., Vol. 89, No. 1, pp. 77-101DOI
5 
Adnan K., Akbar R., Oct. 2019, An analytical study of information extraction from unstructured and multidimensional big data, J. Big Data, Vol. 6, No. 1DOI
6 
Ha H. T., Medved’ M., Nevěřilová Z., Horák A., 2018, Recognition of OCR Invoice Metadata Block Types, in Text, Speech, and Dialogu, Springer International Publishing, pp. 304-312DOI
7 
Zhang J., Ren F., Ni H., Zhang Z., Wang K., Dec. 2019, Research on Information Recognition of VAT Invoice Based on Computer VisionDOI
8 
Zhi X., Shen Z., Zhao B., Jul. 2021, A Method for Identifying the Key Information of Electronic Invoicing in Complex ScenesDOI
9 
Kumar P., Revathy S., 2021, An Automated Invoice Handling Method Using OCR, in Data Intelligence and Cognitive Informatics, Springer Singapore, pp. 243-254DOI
10 
Wang Y., 2022, Intelligent Invoice Identification Technology Based on Zxing Technology, in Lecture Notes in Electrical Engineering, Springer Nature Singapore, pp. 87-93DOI
11 
Ha H. T., Horák A., Mar. 2022, Information extraction from scanned invoice images using text analysis and layout features, Signal Process. Image Commun., Vol. 102, pp. 116601DOI
12 
Hamelers L. H., Jan. 2021, Detecting and explaining potential financial fraud cases in invoice data with Machine Learning.URL
13 
Tutica L., Vineel K. S. K., Mishra S., Mishra M. K., Suman S., 2021, Invoice Deduction Classification Using LGBM Prediction Model, in Lecture Notes in Electrical Engineering, Springer Singapore, pp. 127-137DOI
14 
Bâra S.-V. Oprea and A., Sep. 2021, Machine learning classification algorithms and anomaly detection in conventional meters and Tunisian electricity consumption large datasets, Comput. Electr. Eng., Vol. 94, pp. 107329DOI
15 
Tarawneh A. S., Hassanat A. B., Chetverikov D., Lendak I., Verma C., Apr. 2019, Invoice Classification Using Deep Features and Machine Learning TechniquesDOI
16 
Zhang C., Li B., Edirisinghe E., Smith C., Lowe R., 2022, Extract Data Points from Invoices with Multi-layer Graph Attention Network and Named Entity Recognition, in 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pp. 1-6DOI
17 
Li M., 2022, Smart Accounting Platform Based on Visual Invoice Recognition Algorithm, in 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1436-1439DOI
18 
Ding N., Zhang X., Zhai Y., Li C., Mar. 2021, Risk assessment of VAT invoice crime levels of companies based on DFPSVM: a case study in China, Risk Manage., Vol. 23, No. 1-2, pp. 75-96DOI
19 
Riba P., Dutta A., Goldmann L., Fornes A., Ramos O., Llados J., Sep. 2019, Table Detection in Invoice Documents by Graph Neural NetworksDOI
20 
Bardelli C., Rondinelli A., Vecchio R., Figini S., Nov. 2020, Automatic Electronic Invoice Classification Using Machine Learning Models, Mach. Learn. Knowl. Extr., Vol. 2, No. 4, pp. 617-629DOI
21 
Tang et al. P., Oct. 2020, Anomaly detection in electronic invoice systems based on machine learning, Inf. Sci., Vol. 535, pp. 172-186DOI
22 
Hong J., Yeo H., Cho N.-W., Ahn T., Oct. 2018, Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning, J. Risk Financ. Manag., Vol. 11, No. 4, pp. 70DOI
23 
Baek Y., Lee B., Han D., Yun S., Lee H., Jun. 2019, Character Region Awareness for Text DetectionDOI
24 
Koch G., Zemel R., Salakhutdinov R., 2015, Siamese Neural Networks for One-shot Image Recognition, in Proceedings of the 32 nd International Conference on Machine Learning, pp. 8URL
25 
Kipf T. N., Welling M., 2017, Semi-Supervised Classification with Graph Convolutional Networks.URL
26 
Kumthekar Y. V., 2020, Using ChebConv and B-Spline GNN models for Solving Unit Commitment and Economic Dispatch in a day ahead Energy Trading Market based on ERCOT Nodal ModelURL
27 
Ba D. P. Kingma and J., 2017, Adam: A Method for Stochastic Optimization.URL