Proposes a new OCSR workflow that improves recognition rates by separating adhesive chemical symbols and specifically handling virtual/real wedge bonds using vectorization, achieving 90% exact match vs 82.2% for OSRA baseline.
This paper introduces MolRec, a rule-based system for Optical Chemical Structure Recognition (OCSR). It defines a set of 18 geometric rewrite rules to disambiguate bonds and atoms in vectorised diagram images, demonstrating higher accuracy than the contemporary state-of-the-art (OSRA).
ChemInk introduces a sketch recognition system for chemical diagrams that combines multi-level visual features via a joint Conditional Random Field (CRF), achieving 97.4% accuracy and outperforming CAD tools in user speed.
This paper introduces CLiDE Pro, an advanced OCSR system that segments document images and reconstructs chemical connection tables. It features novel handling for crossing bonds and generic structures, validating performance on a publicly released benchmark of 454 scanned images.
This paper introduces Kekulé-1, one of the first successful Optical Chemical Structure Recognition (OCSR) systems. It details a hybrid approach using neural networks for character recognition and heuristic vectorization for bond detection, achieving 98.9% accuracy on a test set of 524 structures.
This paper details the algorithmic pipeline of OSRA, an open-source tool that converts raster images of chemical diagrams into connection tables (SMILES/SDF). It outlines specific heuristics for page segmentation, vectorization, and atom recognition used in the TREC-CHEM Image2Structure task.
This paper proposes a strategy for interpreting handwritten chemical formulas by converting bitmap images into a dynamic structural graph of quadrilaterals. It achieves ~97% recognition on graphical elements by using recursive ‘specialists’ to identify chemical bonds and rings.
This paper introduces NOMINATE, a probabilistic spatial model that recovers metric coordinates for legislators and roll calls from nominal voting data, demonstrating that a single liberal-conservative dimension explains the vast majority of Congressional voting behavior.
This methodological paper presents a system for digitizing chemical images into SDF files. It utilizes a custom vectorization algorithm and chemical rule validation, achieving 94% accuracy on benchmark datasets compared to 50% for commercial tools.
The CLiDE project presents a foundational architecture for Optical Chemical Structure Recognition (OCSR). It details a three-phase pipeline to convert bitmapped journal pages into chemically significant connection tables, handling complex features like stereochemistry.
This 2003 paper introduces a machine vision approach for extracting chemical metadata from raster images. By using Gabor wavelets for feature extraction and Kohonen networks for classification, it distinguishes between chemical and non-chemical images, as well as ring and non-ring systems, without requiring high-resolution inputs.
This paper presents ChemReader, a fully automated optical structure recognition tool that converts raster images of chemical diagrams into machine-readable formats. It introduces a modified Hough transform for bond detection and a chemical spell checker that improves OCR accuracy from 66% to 87%.