A substantial fraction of chemical knowledge is recorded as 2D diagrams in journals, patents, and textbooks. Optical Chemical Structure Recognition (OCSR) is the task of extracting machine-readable molecular representations from those images: strings like SMILES (a compact text encoding of molecular structure) and InChI (a standardized identifier for chemical substances), or molecular graphs that encode atoms as nodes and bonds as edges. For a longer introduction to the field and its motivations, see the What is OCSR? post.

These notes trace the field from its origins in the early 1990s through to current vision-language approaches. Three broad eras give the collection its shape. The rule-based pioneers (1990s to mid-2010s), including tools like OSRA, MolVec, CLiDE, and Imago, vectorized images and applied hand-coded rules to classify bonds and atoms; their brittleness came from the difficulty of encoding every edge case explicitly. The deep learning transition (roughly 2015 to 2020) replaced those hand-coded rules with models that learned recognition patterns from large synthetic datasets, yielding both image-to-sequence architectures (DECIMER, Img2Mol, Image2SMILES) and image-to-graph architectures (MolGrapher, MolScribe). The current vision-language era (2021 onward), with models like MolParser, GTR-Mol-VLM, and Subgrapher, builds on large pretrained vision-language models to improve generalization across diverse diagram styles and chemical notation conventions.

Beyond the core recognition systems, the collection includes review papers, benchmark and competition write-ups (TREC-Chem 2011, CLEF-IP 2012), and notes on specialized sub-tasks: hand-drawn structure recognition, Markush structure detection, and component-level problems like ring and bond parsing.

For orientation, the two survey papers are the best starting points: rajan-ocsr-review-2020 covers the rule-based era and benchmarks the transition period, while musazade-ocsr-review-2022 picks up the thread with deep learning methods.