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Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture

  • Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.

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Author:Kohulan Rajan, Henning Otto Brinkhaus, Christoph Steinbeck, Achim Zielesny
Parent Title (English):Journal of Cheminformatics
Document Type:Article
Date of Publication (online):2024/07/15
Date of first Publication:2024/07/05
Publishing Institution:Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen
Release Date:2024/07/22
Tag:DECIMER; Deep Learning; Hand-drawn chemical structures; OCSR, Optical Chemical Structure Recognition; Transformer
Issue:16: 78
Departments / faculties:Institute / Institut für biologische und chemische Informatik
Licence (German):License LogoEs gilt das Urheberrechtsgesetz

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