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 $0.92$.

翻译：本研究通过名为ExTTNet的深度学习模型自主提取发票中的产品表格。首先，利用光学字符识别（OCR）技术从发票图像中获取文本，该过程采用Tesseract OCR引擎[37]。随后，通过特征提取方法增加现有特征数量以提升准确性。根据OCR结果中每个文本是否为表格元素进行标注处理。本研究使用多层人工神经网络模型，使用Nvidia RTX 3090显卡进行训练，耗时162分钟。训练结果显示F1得分为0.92。