The field of optical chemical structure recognition (OCSR) deals with the translation of images of chemical structures as they are published in the scientific literature into machine-readable representations of the underlying molecular graph . In the past two years, a variety of deep learning-based OCSR methods has been published, where DECIMER Image-Transformer , Img2Mol and SwinOCSR provide openly available source code and trained models. For the segmentation of chemical structure images from whole pages, the open-source tool DECIMER Segmentation can be used . With the publication of the open-source depiction generation tool RanDepict, efforts have been made to standardize and diversify the training data for deep learning-based OCSR tools . The newest version of DECIMER was trained on more than 400 Million images using the latest Tensor Processing Units available on the Google cloud platform. Currently, DECIMER performs with an accuracy rate of above 90% and is regarded as an important point of reference for future work . Without open databases like PubChem, where one can download over 100 million chemical structures for free, this would not have been possible.