ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images
This addresses the specific problem of automating table text extraction from invoices for document processing applications, but it appears incremental as it builds on existing OCR and neural network methods.
The paper tackles the problem of autonomously extracting table texts from invoice images using a deep learning model called ExTTNet, achieving an F1 score of 0.92 after training for 162 minutes.
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 $0.92$.