In recent years, deep learning-based OCSR methods have become increasingly popular , driven by advancements in computer vision and powerful hardware for training complex models. Deep learning approaches excel in processing chemical structure depictions and can effectively process even distorted representations . This capability provides a competitive edge when developing OCSR methods for hand-drawn chemical structures. Since deep learning algorithms can detect more complex patterns, they are an excellent choice for OCSR applications. Additionally, these methods can be trained with large amounts of diverse data, resulting in improved accuracy and reliability. Deep learning methods encompass a range of both closed-source approaches, such as MSE-DUDL , MICER , Image2SMILES , ABC-Net , Image-to-Graph Transformers , IMG2SMI , Molecular-InChI , and DeepOCSR . On the other hand, several open-source deep learning algorithms have been published, including ChemGrapher , DECIMER Image Transformer , ChemPix , SwinOCSR , Img2Mol , MolScribe , and MolGrapher .