DECIMER 1.0 introduces a Transformer-based architecture coupled with EfficientNet-B3 to solve Optical Chemical Structure Recognition. By using the SELFIES representation (which guarantees 100% valid output strings) and scaling training to over 35 million molecules, it achieves 96.47% exact match accuracy on synthetic benchmarks, offering an open-source solution for mining chemical data from legacy literature.
This paper introduces a convolution-free, end-to-end transformer model for molecular image translation. By replacing CNN encoders with Vision Transformers, it achieves a Levenshtein distance of 6.95 on noisy datasets, compared to 7.49 for ResNet50-LSTM baselines.
This paper introduces ICMDT, a Transformer-based architecture for molecular translation (image-to-InChI). By enhancing the TNT block to fuse pixel, small patch, and large patch embeddings, the model achieves superior accuracy on the Bristol-Myers Squibb dataset compared to CNN-RNN and standard Transformer baselines.
This paper proposes an end-to-end deep learning architecture that translates chemical images directly into molecular graphs using a ResNet-Transformer encoder and a graph-aware decoder. It addresses the limitations of SMILES-based approaches by effectively handling non-atomic symbols (abbreviations) and varying drawing styles found in scientific literature.
A Transformer-based system for optical chemical structure recognition introducing a comprehensive data generation pipeline (FG-SMILES, Markush structures, visual contamination) achieving 79% accuracy on real-world images, outperforming rule-based systems like OSRA.
MICER treats optical chemical structure recognition as an image captioning task, using transfer learning with a fine-tuned ResNet encoder and attention-based LSTM decoder to convert molecular images into SMILES strings, reaching 97.54% sequence accuracy on synthetic data and 82.33% on real-world images.
This empirical study isolates the impact of chemical string representations on image-to-text translation models. It finds that while SMILES offers the highest overall accuracy, SELFIES provides a guarantee of structural validity, offering a trade-off for OCSR tasks.
ChemGrapher replaces rule-based chemical OCR with a deep learning pipeline using semantic segmentation to identify atom and bond candidates, followed by specialized classification networks to resolve stereochemistry and bond multiplicity, reducing error rates compared to OSRA across all tested styles.
DECIMER adapts the “Show, Attend and Tell” image captioning architecture to translate chemical structure images into SMILES strings. By leveraging massive synthetic datasets generated from PubChem, it demonstrates that deep learning can perform optical chemical recognition without complex, hand-engineered rule systems.
A 2021 deep learning system using a two-stage approach for OCSR, encoding images into continuous CDDD embeddings before decoding to SMILES. It leverages extensive data augmentation to handle rotations, distortions, and rendering variations for fast and robust molecular structure recognition.
Proposes a new OCSR workflow that improves recognition rates by separating adhesive chemical symbols and specifically handling virtual/real wedge bonds using vectorization, achieving 90% exact match vs 82.2% for OSRA baseline.
A 2021 image-to-text approach treating OCSR as an image captioning task. It uses Transformers with SELFIES representation to convert molecular structure diagrams into SMILES strings, enabling extraction of visual chemical knowledge from scientific literature.