Our previously published application DECIMER Segmentation 26 was re-used in this work to create a complete extraction workflow. It uses an open implementation of the Mask R-CNN architecture in combination with custom processing steps to segment chemical structure depictions from pages in the scientific literature. Since the original publication, we have refactored the complete codebase, added unit tests and wrapped it up in a Python package that can be installed easily from PyPI , but all underlying models and algorithms remain unchanged. The DECIMER Segmentation model was trained on manually annotated data using TensorFlow 2.3.0, but it has been updated to work with TensorFlow 2.10.0 in accordance with the other DECIMER components. The source code and the model are available on GitHub 47 and Zenodo . For further information about DECIMER Segmentation, we would like to refer to the original publication .