DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications

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 extra...

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Veröffentlicht in:Nature communications Jg. 14; H. 1; S. 5045 - 18
Hauptverfasser: Rajan, Kohulan, Brinkhaus, Henning Otto, Agea, M. Isabel, Zielesny, Achim, Steinbeck, Christoph
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 19.08.2023
Nature Publishing Group
Nature Portfolio
Schlagworte:
631/114/1305
631/114/2164
631/114/2406
639/638/403
639/638/630
Applications programs
Automation
Computer vision
Datasets
Deep learning
Humanities and Social Sciences
Image segmentation
Information processing
Information retrieval
multidisciplinary
Natural language processing
Science
Science (multidisciplinary)
Source code
Translating
ISSN:2041-1723, 2041-1723
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Zusammenfassung: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 . Chemical structures are typically published as nonmachine-readable images in scientific literature. Here, the authors present DECIMER.ai, an open platform for translating chemical structures in publications into machine-readable representations.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-40782-0