DECIMER Image Transformer achieves competitive results on most benchmark datasets compared to the other open OCSR tools (Figure ), showing no performance degradation due to slight image distortions while confirming the lack of distortion robustness of the rule-based systems. In addition, the rule-based systems fail to correctly recognise the structure depictions with a low image resolution (see USPTO_big and Indigo in Supplementary Table ). The SwinOCSR model does not achieve any outstanding results in our benchmarks -but it needs to be mentioned that its developers stated that their model does not perform well on real-world data which is likely due to a lack of diversity in their training data. Appropriate assessment of failure rates is of particular importance for machine learning applications (Figure The DECIMER Image Transformer model has never been trained on hand-written chemical structure depictions. However, for a benchmark dataset that only consists of hand-drawn chemical structures (DECIMER Hand-drawn image dataset outlined in the Methods section), it recognises 27% of the structures perfectly and achieves an average Tanimoto similarity of 0.69, whereas all alternative open tools perform worse (see Supplementary Table ). Moreover, when the model is fine-tuned with a training dataset of images with augmentations that make them appear hand-drawn-like (see Figure and Supplementary Table ), the proportion of perfect predictions grows significantly to 60% (i.e., an increase of 33%), corresponding to a remarkable average Tanimoto similarity increase of plus 0.2 to 0.89.