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 found in traditional laboratory notebooks or for 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. To evaluate the model's performance, a benchmark was performed using a real-world dataset of hand-drawn chemical structures. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.
Verfasserangaben: | Kohulan Rajan, Henning Otto Brinkhaus, Achim Zielesny, Christoph Steinbeck |
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DOI: | https://doi.org/10.26434/chemrxiv-2024-7ch9f |
Dokumentart: | Preprint |
Sprache: | Englisch |
Datum der Veröffentlichung (online): | 15.07.2024 |
Jahr der Erstveröffentlichung: | 2024 |
Veröffentlichende Institution: | Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen |
Datum der Freischaltung: | 23.07.2024 |
Freies Schlagwort / Tag: | DECIMER; Deep Learning; OCSR, Optical Chemical Structure Recognition; Transformer |
Fachbereiche / Institute: | Institute / Institut für biologische und chemische Informatik |
Lizenz (Deutsch): |