
End-to-End Transformer for Molecular Image Captioning
Vision Transformer encoder with Transformer decoder for molecular image-to-InChI translation, achieving state-of-the-art …

Vision Transformer encoder with Transformer decoder for molecular image-to-InChI translation, achieving state-of-the-art …

An end-to-end framework (RCGD) and unambiguous markup language (SSML) for recognizing complex handwritten chemical …

A Transformer-based model (ICMDT) for converting chemical structure images into InChI text strings using a novel Deep …

A deep learning model that converts molecular images directly into graph structures, enabling recognition of abbreviated …
Transformer-based OCSR using a novel synthetic data generation pipeline for robust molecular image interpretation across …
Encoder-decoder model using pre-trained ResNet and attention-based LSTM to translate molecular images into SMILES, …
Deep learning OCSR tool using YOLOv5 and MobileNetV2 to extract machine-readable molecular structures from scientific …
Patch-based CNN method for detecting Markush structures in chemical images, addressing low signal-to-noise ratios in …
Systematization of OCSR evolution from rule-based systems to deep learning, highlighting the paradigm shift to image …
Ablation study comparing SMILES, DeepSMILES, SELFIES, and InChI for OCSR. SMILES achieves highest accuracy; SELFIES …
Deep learning model using Swin Transformer and Focal Loss for OCSR, achieving 98.58% accuracy on synthetic benchmarks.

Deep learning OCSR method using semantic segmentation and classification CNNs to reconstruct chemical graphs with …