Our model has been trained on three datasets: 1) printed chemical structure diagrams to SMILES strings conversion with 5 million molecules from PubChem by SwinOCSR , 2) our synthetic dataset containing about 320K images of 80K molecules (Synt-80K), 3) and our real dataset with about 2K images for 200 molecules. We hypothesize that each stage benefits the final model in terms of performance. We perform an ablation study on these stages. For training stages involving synthetic-80k pre-training, we do not do much hyperparameter search as long as the model converges at a reasonable rate and reaches 99.7% validation token level accuracy. We used hyperparameter optimizers for training involving the real-200 dataset for learning rate, epoch, and learning rate decay. The hyperparameter is searched for each setting independently. With teacher forcing, we used a fixed validation for around 30 trials on token-level validation accuracy.