ExTTNet：一种从发票图像中提取表格文本的深度学习算法
ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images
AI总结：
提出ExTTNet深度学习算法，结合OCR和特征提取，用多层神经网络从发票图像中自动提取表格文本，F1分数达0.92。
摘要：
在这项工作中，通过一个名为ExTTNet的深度学习模型自主获取发票中的产品表格。首先，使用光学字符识别（OCR）技术从发票图像中获取文本，该过程使用了Tesseract OCR引擎[37]。随后，通过特征提取方法增加现有特征的数量以提高准确性。根据OCR获得的每个文本是否为表格元素进行标注。本研究采用多层人工神经网络模型。训练使用Nvidia RTX 3090显卡进行，耗时162分钟。训练结果为F1分数达到0.92。
英文摘要：
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 $0.92$.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
