  Most molecular diagram parsers recover chemical structure from raster images
(e.g., PNGs). However, many PDFs include commands giving explicit locations and
shapes for characters, lines, and polygons. We present a new parser that uses
these born-digital PDF primitives as input. The parsing model is fast and
accurate, and does not require GPUs, Optical Character Recognition (OCR), or
vectorization. We use the parser to annotate raster images and then train a new
multi-task neural network for recognizing molecules in raster images. We
evaluate our parsers using SMILES and standard benchmarks, along with a novel
evaluation protocol comparing molecular graphs directly that supports automatic
error compilation and reveals errors missed by SMILES-based evaluation. On the
synthetic USPTO benchmark, our born-digital parser obtains a recognition rate
of 98.4% (1% higher than previous models) and our relatively simple neural
parser for raster images obtains a rate of 85% using less training data than
existing neural approaches (thousands vs. millions of molecules).
