A key application of deep learning methods in chemistry is the mining of printed and hand-written documents for information on chemical compounds. Mining of past publications, for example, can augment present open-access databases . While this information can often be found in printed literature, it is typically presented in unstructured, human-readable formats like text and images. Manually curating and organising this information to fill the database gaps is error-prone and time-consuming . Therefore, automation is necessary to improve accuracy and efficiency . A key task is detecting and interpreting chemical structure depictions to translate them into machine-readable formats, commonly referred to as Optical Chemical Structure Recognition (OCSR) .