In comparison with alternative open OCSR tools, DECIMER Image Transformer performs with high accuracy. Apart from Img2Mol's performance on its in-domain test data, MolScribe's performance on USPTO data (which may be part of the system's training data ) and the performance of MolVec on the non-distorted JPO, CLEF and USPTO datasets, DECIMER Image Transformer performs outstandingly well on all benchmark datasets without any significant differences between non-distorted and distorted images. It is particularly striking that the system's severe and catastrophic failure rates are very low. The system also achieves a comparative peak performance when benchmarked against the DECIMER Hand-drawn image dataset, which is especially interesting since there has not been a single hand-drawn structure in the training data. Thus, this model may be applied to extract chemical structures from hand-drawn images in the future. This may become particularly relevant for translating chemical publications from 50 years ago since a lot of the chemical structures from that time were hand-drawn using templates. Although the predictions might not be perfect in all cases, a similar prediction considerably reduces the amount of manual work when mining chemical structures from printed literature. The outlined success rates of DECIMER Image Transformer demonstrate not only robust performance, but also superior generalisation capabilities due to a training data diversification strategy with highly diverse structure depictions generated by our OCSR training data generation tool RanDepict 37 : It ensures that the full diversity of depiction features is properly represented that CDK , RDKit 29 , Indigo and PIKAChU 31 have to offer.