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.

中文翻译：

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图像和扫描文档中光学化学结构识别技术和模型的回顾

从图像中提取化学式有一段时间不是计算机视觉任务的重中之重。输入和预测方面的复杂性使得这项任务对传统的人工智能和机器学习问题具有挑战性。对于卷积分析来说似乎微不足道的二进制输入图像不容易分类，因为提供的样本不能代表给定的分子：为了描述相同的公式，可以使用彼此不相似的各种图形表示。考虑到分子的多样性，问题从分类转向公式生成，这使得自然语言处理 (NLP) 成为有效解决方案的良好候选者。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的演变，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提供了理想的结果，但作者提到了各种场景可能存在的问题，并为进一步发展提供了建议。