  Chemical structure extraction from documents remains a hard problem due to
both false positive identification of structures during segmentation and errors
in the predicted structures. Current approaches rely on handcrafted rules and
subroutines that perform reasonably well generally, but still routinely
encounter situations where recognition rates are not yet satisfactory and
systematic improvement is challenging. Complications impacting performance of
current approaches include the diversity in visual styles used by various
software to render structures, the frequent use of ad hoc annotations, and
other challenges related to image quality, including resolution and noise. We
here present end-to-end deep learning solutions for both segmenting molecular
structures from documents and for predicting chemical structures from these
segmented images. This deep learning-based approach does not require any
handcrafted features, is learned directly from data, and is robust against
variations in image quality and style. Using the deep-learning approach
described herein we show that it is possible to perform well on both
segmentation and prediction of low resolution images containing moderately
sized molecules found in journal articles and patents.
