Over the past few years, deep learning methods have been used extensively to conduct OCSR for detecting and converting chemical structure depictions from printed literature . With improvements in computer vision and language models, the field has seen a lot of development . Molecular structures can be represented in images in various ways, using many different drawing styles. When representations of a variety of depiction styles are included in the training data, a data-driven deep-learning approach can be applied to reach a high degree of robustness and flexibility. Rule-based OCSR algorithms that are not based on deep learning have been shown to lack robustness and tend to fail when small distortions are added to the images in common benchmark datasets .