A machine-learning system learns to adapt its actions based on given environment information. Consequently, the quality of the environment information is a crucial factor for the system learning to solve a specific task . Machine-learning systems are able to learn best when the input data they receive is similar to the data they have been trained on. In the case of most deep learning-based OCSR systems, the training data consists of images with depictions of chemical structures which are mapped to string representations of the underlying molecular graph. To be able to generalise well across a variety of different depiction styles, a machine-learning model needs to be trained on these depiction styles as well. Additionally, chemical structure depictions often contain non-structural elements like atom numbering or mechanism arrows which need to be considered as common noise types (Figure ). This is particularly relevant for real-world chemical data extraction applications, since the only openly available deep learning-based segmentation tool for chemical structures, DECIMER Segmentation, tends to include these non-structural elements in its output segments . Hence, there is a need for a tool for the generation of chemical structure depictions of various depiction styles with additional non-structural elements.