MolGrapher introduces a three-stage pipeline (keypoint detection, supergraph construction, GNN classification) for recognizing chemical structures from images. It achieves 91.5% accuracy on USPTO by treating molecules as graphs, and introduces the USPTO-30K benchmark.
MolMole unifies molecule detection, reaction parsing, and structure recognition into a single vision-based pipeline, achieving top performance on a newly introduced 550-page benchmark by processing full documents without external layout parsers.
MolScribe reformulates molecular recognition as an image-to-graph generation task, explicitly predicting atom coordinates and bonds to better handle stereochemistry and abbreviated structures compared to image-to-SMILES baselines.
ABC-Net reformulates molecular image recognition as a keypoint detection problem. By predicting atom/bond centers and properties via a single Fully Convolutional Network, it achieves >94% accuracy with high data efficiency.
Proposes a Random Conditional Guided Decoder (RCGD) and a Structure-Specific Markup Language (SSML) to handle the ambiguity and complexity of handwritten chemical structure recognition, validated on a new benchmark dataset (EDU-CHEMC) with 50,000 handwritten images.
MolMiner replaces traditional rule-based vectorization with a deep learning object detection pipeline (YOLOv5) to extract chemical structures from PDFs. It outperforms open-source baselines on four benchmarks and introduces a new real-world dataset of 3,040 images.
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.
This paper reviews three decades of OCSR development, transitioning from rule-based heuristics to early deep learning approaches. It includes a benchmark study comparing the performance of three open-source tools (OSRA, Imago, MolVec) on four diverse datasets.
Describes chemoCR, a system that converts bitmap chemical diagrams into connection tables using a pipeline of texture-based vectorization, OCR, and a rule-based expert system, achieving 65.6% perfect recall on the TREC 2011 task.
ChemReader achieved 93% accuracy on the TREC 2011 Image-to-Structure task, with detailed error analysis revealing the need for improved chemical intelligence in bond recognition and node merging algorithms.
A resource paper detailing the CLEF-IP 2012 benchmarking lab. It introduces specific IR tasks for patent processing along with ground-truth datasets.
Describes the MolRec system’s performance in the CLEF 2012 Chemical Structure Recognition task, detailing its rule-based vectorization engine and analyzing failure modes like touching characters and complex bond types.