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ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images

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arxiv 2402.02246 v1 pith:WPE5WG4V submitted 2024-02-03 cs.CV cs.AIcs.IRcs.LGcs.NE

Adem Akdoğan , Murat Kurt This is my paper · ORCID
classification cs.CVcs.AIcs.IRcs.LGcs.NE
keywords obtaineddeepexttnetimagesinvoicelearningmodelprocess
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

In this work, product tables in invoices are obtained autonomously via a deep learning model, which is named as ExTTNet. Firstly, text is obtained from invoice images using Optical Character Recognition (OCR) techniques. Tesseract OCR engine [37] is used for this process. Afterwards, the number of existing features is increased by using feature extraction methods to increase the accuracy. Labeling process is done according to whether each text obtained as a result of OCR is a table element or not. In this study, a multilayer artificial neural network model is used. The training has been carried out with an Nvidia RTX 3090 graphics card and taken $162$ minutes. As a result of the training, the F1 score is $0.92$.

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Cited by 2 Pith papers

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