After training each model, it was tested against the DECIMER hand-drawn chemical structure images dataset for accuracy and similarity. The number of valid predictions, i.e. the returned SMILES string was syntactically valid and could be parsed into a molecular structure, is also measured. Table provides the final average values for overall predictions by comparing each predicted structure with the original structure. As can be seen from the above results, the DECIMER model overall performs much better than other deep learning models. According to the results, the rule-based methods perform significantly worse than all the currently available deep learning methods. It is primarily due to the handcrafted rules that were developed for chemical structure representations found in printed literature, as when we deployed them on a hand-drawn dataset, they were not able to function properly since they are not as flexible as the deep learning tools when it comes to processing hand-drawn chemical structures. While deep learning models tend to display a higher level of robustness on this dataset, the number of valid predictions generated by these models is significantly higher than those generated by rule-based methods since deep learning models are likely to pick up on patterns, contexts and subtleties in the hand-drawn structures since they are more robust to noise and variability because they learn the patterns directly from the training data rather than having hardcoded rules. As a result, they can take advantage of a lot more contextual data in the input to make predictions.