Vast quantities of scientific information are hidden in primary scientific publications and not available as curated data in scientific databases. Making such information publicly available to support open science and open innovation is a challenge that has to be solved. In this dissertation, state-of-the-art deep learning models for optical chemical structure recognition and chemical information processing have been implemented to rediscover this information and retrieve it automatically.
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