MarkushGlyph与OCSRGlyph：改进的化学结构识别
MarkushGlyph and OCSRGlyph: Improved Chemical Structure Recognition
AI总结：
本研究将化学结构识别视为图像到文本转换任务，提出OCSRGlyph与MarkushGlyph模型，前者优化OCSR性能，后者针对Markush结构采用整体视觉语言建模，还提出新指标解决Markush结构翻译的准确性判定问题。
AI中文摘要：
化学结构以图像形式出现在专利和科学文献中，为了实现数据库索引或构建机器学习模型训练集等程序化用途，必须将其转换为线性表示法。该任务的两种常见形式是：识别单个分子图像的光学化学结构识别（Optical Chemical Structure Recognition，OCSR），以及识别代表分子家族的Markush结构。虽然前者的现有研究已较为成熟，但Markush结构解析仍是一项具有挑战性的任务。本研究将这两项任务均视为图像到文本的转换问题，提出了达到当前最优水平的OCSR模型OCSRGlyph，通过仔细考虑立体化学提升了性能；针对Markush任务，引入了将整个Markush结构作为图像读取的视觉语言模型MarkushGlyph，与现有常采用多阶段分别处理视觉和文本输入内容的系统形成对比；最后，提出了一种用于判定Markush结构翻译准确性的新指标，以应对现有指标存在的失效模式。
英文摘要：
Chemical structures appear in patents and the scientific literature as images. For programmatic usage, such as indexing in databases or constructing machine learning model training sets, they must be transformed into line notations. The two common forms of this task are translating an image of a single molecule (optical chemical structure recognition - OCSR) and translating a Markush structure that represents a family of molecules. While prior work in the former case is quite mature, Markush structure parsing remains a challenging task. In this work, we treat both tasks as an image-to-text translation problem. We then propose OCSRGlyph, a state-of-the-art OCSR model, improving performance over prior methods by carefully considering stereochemistry. For the Markush task, we introduce MarkushGlyph, a vision-language model that reads the entire Markush structure as an image. This contrasts with prior systems, which often use multiple stages to separately process visual and text input content. Finally, we introduce a new metric for determining the accuracy of Markush structure translations, handling failure modes present in prior metrics.
