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, leveraging transfer learning with a fine-tuned ResNet encoder and attention-based LSTM decoder to convert molecular images into SMILES strings, significantly outperforming rule-based and previous deep learning methods.

MolMiner replaces traditional rule-based vectorization with a deep learning object detection pipeline (YOLOv5) to extract chemical structures from PDFs. It achieves state-of-the-art performance on benchmarks and introduces a new real-world dataset of 3,040 images.

Proposes a patch-based image processing pipeline using Inception V3 to filter Markush structures from chemical documents, significantly outperforming traditional fixed-feature (ORB) methods on low-SNR images.

This systematization paper traces the history of OCSR, comparing early rule-based systems like OSRA with modern deep learning approaches like DECIMER. It highlights the shift from image classification to image captioning and identifies critical gaps in dataset standardization and evaluation metrics.

This methodological 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.

Proposes an end-to-end architecture replacing standard CNN backbones with Swin Transformer to capture global image context. Introduces Multi-label Focal Loss to handle severe token imbalance in chemical datasets.

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, significantly outperforming OSRA.

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.

This paper presents a two-stage deep learning pipeline to extract chemical structures from documents and convert them to SMILES strings. By training on large-scale synthetic data, the method overcomes the brittleness of rule-based systems and demonstrates high accuracy even on low-resolution and noisy input images.