Using this method, we can achieve recognition performance with high confidence in hand-drawn chemical structure depictions. Furthermore, we improved the recognition results' accuracy by enhancing the DECIMER Image Transformer model. To determine which encoder-decoder model performs best on the same data set, three different models with different configurations of encoder-decoder architectures have been investigated in this study. Subsequently, the best-performing model was trained on datasets of hand-drawn-like chemical structure depictions of four different sizes generated using RanDepict. Finally, the best-trained model was benchmarked against other deep learning-based OCSR methods using a hand-drawn chemical structure dataset. As compared to other openly available OCSR applications, our approach produces better results, with an accuracy of 73.25% and a Tanimoto average of 0.94. This approach can be used to develop accurate and robust OCSR pipelines for real-world applications. Our hand-drawn chemical structure detection model, which we call the DECIMER hand-drawn model, has been incorporated into the DECIMER module and made publicly available. These resources are provided under permissive licenses and accompanied by comprehensive documentation.