Musazade Fidan, Jamalova Narmin, Hasanov Jamaladdin
School of Engineering and Applied Science, The George Washington University, Washington, DC, United States.
School of IT and Engineering, ADA University, Baku, Azerbaijan.
J Cheminform. 2022 Sep 9;14(1):61. doi: 10.1186/s13321-022-00642-3.
Extraction of chemical formulas from images was not in the top priority of Computer Vision tasks for a while. The complexity both on the input and prediction sides has made this task challenging for the conventional Artificial Intelligence and Machine Learning problems. A binary input image which might seem trivial for convolutional analysis was not easy to classify, since the provided sample was not representative of the given molecule: to describe the same formula, a variety of graphical representations which do not resemble each other can be used. Considering the variety of molecules, the problem shifted from classification to that of formula generation, which makes Natural Language Processing (NLP) a good candidate for an effective solution. This paper describes the evolution of approaches from rule-based structure analyses to complex statistical models, and compares the efficiency of models and methodologies used in the recent years. Although the latest achievements deliver ideal results on particular datasets, the authors mention possible problems for various scenarios and provide suggestions for further development.
一段时间以来，从图像中提取化学式并非计算机视觉任务的首要优先级。输入和预测方面的复杂性使得这项任务对于传统的人工智能和机器学习问题而言具有挑战性。对于卷积分析来说看似简单的二进制输入图像却不易分类，因为所提供的样本并不代表给定的分子：为了描述同一个化学式，可以使用各种彼此并不相似的图形表示。考虑到分子的多样性，问题从分类转变为化学式生成问题，这使得自然语言处理（NLP）成为有效解决方案的一个不错选择。本文描述了从基于规则的结构分析到复杂统计模型的方法演变，并比较了近年来使用的模型和方法的效率。尽管最新成果在特定数据集上取得了理想结果，但作者提到了各种场景下可能存在的问题，并为进一步发展提供了建议。
