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Existing visual parsers for molecule diagrams translate pixel-based rasterimages such as PNGs to chemical structure representations (e.g., SMILES).However, PDFs created by word processors including LaTeX and Word provideexplicit locations and shapes for characters, lines, and polygons. We extractsymbols from born-digital PDF molecule images and then apply simple graphtransformations to capture both visual and chemical structure in editableChemDraw files (CDXML). Our fast ( PDF $\rightarrow$ visual graph $\rightarrow$chemical graph ) pipeline does not require GPUs, Optical Character Recognition(OCR) or vectorization. We evaluate on standard benchmarks using SMILESstrings, along with a novel evaluation that provides graph-based metrics anderror compilation using LgEval. The geometric information in born-digital PDFsproduces a highly accurate parser, motivating generating training data forvisual parsers that recognize from raster images, with extracted graphics,visual structure, and chemical structure as annotations. To do this we renderSMILES strings in Indigo, parse molecule structure, and then validaterecognized structure to select correct files.

ChemScraper: Graphics Extraction, Molecular Diagram Parsing, and Annotated Data Generation for PDF Images