SwinOCSR is a model that can translate printed molecular structures into DeepSMILES. It uses a Swin Transformer as encoder and a transformer encoder-decoder pair as decoder . SwinOCSR is trained with an image size of 224×224 with 4.5 million images, while both the validation and testing sets contain 250,000 images, each representing a total of 5 million molecules in their dataset. To address the imbalance of the tokens distribution (e.g. in the dataset, the token C (carbon) appears much more than = (double bond)), SwinOCSR uses focal loss instead of normal cross-entropy loss. In their testing, the accuracy is 98.6% accuracy. However, on a real-world testing set, they only achieved 25.0% accuracy with a 0.5975 Tanimoto score.