Taking these considerations into account, we present an advanced deep-learning method for accurate hand-drawn chemical structure recognition. We introduce an encoder-decoder model that combines the EfficientNetV2 Convolutional Neural Network (CNN) with a Transformer Decoder-only model. This combination aims to identify and transform hand-drawn chemical structures into a machine-readable file format with higher confidence. Our approach builds upon the DECIMER image transformer [12], a deep learning-based OCSR method developed for extracting chemical structural data from printed literature. There is a growing interest in identifying hand-drawn chemical structure depictions, as this has the potential to streamline the automated digitisation of laboratory notebooks .