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 $0.92$.
Abstract (translated)
在这项工作中，通过一个名为ExTTNet的深度学习模型，在发票中自动获取产品表是通过深度学习模型获得的。首先，使用光字符识别（OCR）技术从发票图像中获取文本。 Tesseract OCR 引擎 [37] 用于这个过程。然后，通过使用特征提取方法增加现有特征的数量来提高准确性。根据提取的 OCR 结果文本是否为表元素进行标签处理。在这项研究中，使用了多层人工神经网络模型。训练过程使用Nvidia RTX 3090图形芯片进行，用时162分钟。训练后，F1得分达到了0.92。
URL
https://arxiv.org/abs/2402.02246
