  Molecular structures are always depicted as 2D printed form in scientific
documents like journal papers and patents. However, these 2D depictions are not
machine-readable. Due to a backlog of decades and an increasing amount of these
printed literature, there is a high demand for the translation of printed
depictions into machine-readable formats, which is known as Optical Chemical
Structure Recognition (OCSR). Most OCSR systems developed over the last three
decades follow a rule-based approach where the key step of vectorization of the
depiction is based on the interpretation of vectors and nodes as bonds and
atoms. Here, we present a practical software MolMiner, which is primarily built
up using deep neural networks originally developed for semantic segmentation
and object detection to recognize atom and bond elements from documents. These
recognized elements can be easily connected as a molecular graph with
distance-based construction algorithm. We carefully evaluate our software on
four benchmark datasets with the state-of-the-art performance. Various real
application scenarios are also tested, yielding satisfactory outcomes. The free
download links of Mac and Windows versions are available: Mac:
https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/mac/PharmaMind-mac-latest-setup.dmg
and Windows:
https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/win/PharmaMind-win-latest-setup.exe
