论文标题
用于化学结构识别的图像到电场变压器
Image-to-Graph Transformers for Chemical Structure Recognition
论文作者
论文摘要
数十年来，化学知识已经在书面文本中发表，并且已经有许多尝试使其可以通过将这种自然语言文本转换为结构化格式来访问它。尽管图像中通常表示的发现的化学本身是最重要的部分，但文献中对分子结构的正确识别仍然是一个严重的问题，因为它们通常被缩写以降低复杂性并以许多不同的样式绘制。在本文中，我们提出了一个深度学习模型，以从图像中提取分子结构。所提出的模型旨在将分子图像直接转换为相应的图，从而使其能够处理非原子符号进行缩写。同样，通过端到端的学习方法，它可以完全利用来自各种来源的许多开放图像 - 分子对数据，因此，与其他工具相比，它对图像样式变化更为强大。实验结果表明，所提出的模型的表现优于我们分别从文献中收集的众所周知的基准数据集和大型分子图像，其相对改进为17.1％和12.8％。
For several decades, chemical knowledge has been published in written text, and there have been many attempts to make it accessible, for example, by transforming such natural language text to a structured format. Although the discovered chemical itself commonly represented in an image is the most important part, the correct recognition of the molecular structure from the image in literature still remains a hard problem since they are often abbreviated to reduce the complexity and drawn in many different styles. In this paper, we present a deep learning model to extract molecular structures from images. The proposed model is designed to transform the molecular image directly into the corresponding graph, which makes it capable of handling non-atomic symbols for abbreviations. Also, by end-to-end learning approach it can fully utilize many open image-molecule pair data from various sources, and hence it is more robust to image style variation than other tools. The experimental results show that the proposed model outperforms the existing models with 17.1 % and 12.8 % relative improvement for well-known benchmark datasets and large molecular images that we collected from literature, respectively.
