TY  - JOUR
A1  - Rajan, Kohulan
A1  - Brinkhaus, Henning Otto
A1  - Steinbeck, Christoph
A1  - Zielesny, Achim
T1  - Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture
T2  - Journal of Cheminformatics
N2  - 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.
KW  - DECIMER
KW  - Hand-drawn chemical structures
KW  - OCSR, Optical Chemical Structure Recognition
KW  - Transformer
KW  - Deep Learning
Y1  - 2024
UR  - https://whge.opus.hbz-nrw.de/frontdoor/index/index/docId/4541
VL  - 2024
IS  - 16: 78
ER  -