A 2025 Vision-Language Model for OCSR that uses graph traversal chain-of-thought reasoning and a two-stage SFT plus GRPO training scheme to handle both printed molecules (including chemical abbreviations like Ph and Et) and hand-drawn structures, achieving strong performance on the new MolRec-Bench benchmark.
Proposes the ‘Optical Chemical Structure Understanding’ (OCSU) task to translate molecular images into multi-level descriptions (motifs, IUPAC, SMILES). Introduces the Vis-CheBI20 dataset and two paradigms: DoubleCheck (OCSR-based) and Mol-VL (OCSR-free).
Cohen et al. introduce Spherical CNNs that achieve SO(3)-equivariance by defining cross-correlation on the sphere and rotation group, computed efficiently via generalized FFT algorithms from non-commutative harmonic analysis.
We trace the history of Page Stream Segmentation (PSS) through three eras (Heuristic, Encoder, and Decoder) and explain how privacy-preserving, localized LLMs enable true semantic processing.
PubMed-OCR provides 1.5M pages of scientific articles with comprehensive OCR annotations and bounding boxes to support layout-aware modeling and document analysis.
ChemDFM-X is a multimodal chemical foundation model that integrates five non-text modalities (2D graphs, 3D conformations, images, MS2 spectra, IR spectra) into a single LLM decoder. It overcomes data scarcity by generating a 7.6M instruction-tuning dataset through approximate calculations and model predictions, establishing strong baseline performance across multiple modalities.
Deep dive into 24 image-to-sequence OCSR methods (2019-2025), comparing encoder-decoder architectures, molecular string representations, training scale, and hardware requirements.
This paper presents a multimodal search system that facilitates passage-level retrieval of chemical reactions and molecular structures by linking diagrams, text, and reaction records extracted from scientific PDFs.
OCSAug uses Denoising Diffusion Probabilistic Models (DDPM) and the RePaint algorithm with custom masking to generate synthetic hand-drawn chemical structure images, improving OCSR performance by 1.918-3.820x on the DECIMER benchmark.
Introduces AtomLenz, an OCSR tool that combines object detection with a molecular graph constructor. Features a novel weakly supervised training scheme (ProbKT*) to learn atom-level localization from SMILES-only data, achieving state-of-the-art results on hand-drawn images.
Comprehensive evaluation of 8 optical chemical structure recognition tools using a newly curated dataset of 2,702 patent images. Proposes ChemIC, a ResNet-50 classifier to route images to specialized tools based on content type, demonstrating that no single tool excels at all tasks.
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.