TY  - JOUR
A1  - Rajan, Kohulan
A1  - Brinkhaus, Henning Otto
A1  - Agea, M. Isabel
A1  - Zielesny, Achim
A1  - Steinbeck, Christoph
T1  - DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications
T2  - Nature Communications
N2  - The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai.
KW  - machine learning
KW  - artificial intelligence
KW  - AI
KW  - optical chemical structure recognition
KW  - OCSR
Y1  - 2023
UR  - https://whge.opus.hbz-nrw.de/frontdoor/index/index/docId/4477
VL  - 2023
IS  - 14: 5045
ER  -