ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from
  Invoice Images
Adem Akdoğan Murat Kurt
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 $0.92$.