  Identifying the chemical structure from a graphical representation, or image,
of a molecule is a challenging pattern recognition task that would greatly
benefit drug development. Yet, existing methods for chemical structure
recognition do not typically generalize well, and show diminished effectiveness
when confronted with domains where data is sparse, or costly to generate, such
as hand-drawn molecule images. To address this limitation, we propose a new
chemical structure recognition tool that delivers state-of-the-art performance
and can adapt to new domains with a limited number of data samples and
supervision. Unlike previous approaches, our method provides atom-level
localization, and can therefore segment the image into the different atoms and
bonds. Our model is the first model to perform OCSR with atom-level entity
detection with only SMILES supervision. Through rigorous and extensive
benchmarking, we demonstrate the preeminence of our chemical structure
recognition approach in terms of data efficiency, accuracy, and atom-level
entity prediction.
