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In chemistry-related disciplines, a vast repository of molecular structural data has been documented in scientific publications but remains inaccessible to computational analyses owing to its non-machine-readable format. Optical chemical structure recognition (OCSR) addresses this gap by converting images of chemical molecular structures into a format accessible to computers and convenient for storage, paving the way for further analyses and studies on chemical information. A pivotal initial step in OCSR is automating the noise-free extraction of molecular descriptions from literature. Despite efforts utilising rule-based and deep learning approaches for the extraction process, the accuracy achieved to date is unsatisfactory. To address this issue, we introduce a deep learning model named YoDe-Segmentation in this study, engineered for the automated retrieval of molecular structures from scientific documents. This model operates via a three-stage process encompassing detection, mask generation, and calculation. Initially, it identifies and isolates molecular structures during the detection phase. Subsequently, mask maps are created based on these isolated structures in the mask generation stage. In the final calculation stage, refined and separated mask maps are combined with the isolated molecular structure images, resulting in the acquisition of pure molecular structures. Our model underwent rigorous testing using texts from multiple chemistry-centric journals, with the outcomes subjected to manual validation. The results revealed the superior performance of YoDe-Segmentation compared to alternative algorithms, documenting an average extraction efficiency of 97.62%. This outcome not only highlights the robustness and reliability of the model but also suggests its applicability on a broad scale., (© 2023. The Author(s).)