Benchmark

ChemReco automates the recognition of hand-drawn chemical structures using a synthetic data pipeline and an EfficientNet+Transformer architecture, achieving 96.90% accuracy on C-H-O molecules.

DECIMER.ai addresses the lack of open tools for Optical Chemical Structure Recognition (OCSR) by providing a comprehensive, deep-learning-based workflow. It features a novel data generation pipeline (RanDepict), a web application, and models for segmentation and recognition that rival or exceed proprietary solutions.

Proposes a new architecture (DGAT) to resolve global context loss in chemical structure recognition. Introduces Cascaded Global Feature Enhancement and Sparse Differential Global-Local Attention, achieving 84.0% BLEU-4 and handling complex chiral structures implicitly.

This paper presents an enhanced deep learning architecture for Optical Chemical Structure Recognition (OCSR) specifically optimized for hand-drawn inputs. By pairing an EfficientNetV2 encoder with a Transformer decoder and training on over 150 million synthetic images, the model achieves 73.25% exact match accuracy on a real-world hand-drawn benchmark of 5,088 images.

MMSSC-Net introduces a multi-stage cognitive approach for OCSR, utilizing a SwinV2 encoder and GPT-2 decoder to recognize atomic and bond sequences. It achieves 75-98% accuracy across benchmark datasets by handling varying image resolutions and noise through fine-grained perception of atoms and bonds.

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.