HMM-based method for recognizing online handwritten chemical symbols using 11-dimensional local features including derivatives, curvature, and linearity. Achieves 89.5% top-1 accuracy and 98.7% top-3 accuracy on a custom dataset of 64 chemical symbols.
A 2021 deep learning system using a two-stage approach for OCSR, encoding images into continuous CDDD embeddings before decoding to SMILES. It leverages extensive data augmentation to handle rotations, distortions, and rendering variations for fast and robust molecular structure recognition.
Yang et al. propose a two-level recognition system for handwritten chemical formulas, combining global structural analysis to identify substances with local character recognition using ANNs, achieving ~96% accuracy on a dataset of 1197 expressions.
A three-level grammatical framework (formula, molecule, text) for parsing online handwritten chemical formulas, generating semantic graphs that capture both connectivity and layout using context-free grammars and HMMs.
Proposes a novel two-level algorithm for on-line handwritten chemical expression recognition, combining substance-level matching with character-level segmentation to achieve 96% accuracy.
This paper proposes a double-stage architecture using SVM for rough classification and HMM for fine recognition. It features a novel Point Sequence Reordering (PSR) algorithm that significantly improves accuracy on organic ring structures.
Proposes a unified statistical framework for recognizing both inorganic and organic handwritten chemical expressions. Introduces the Chemical Expression Structure Graph (CESG) and uses a weighted direction graph search for structural analysis, achieving 83.1% top-5 accuracy on a large proprietary dataset.
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.
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.
Benchmarks OSRA on CLEF-IP 2012 patent data, showing native image processing improves precision from 0.433 to 0.708 over external splitting tools. Describes OSRA’s pairwise distance algorithm for segmentation that handles overlapping molecules better than bounding boxes.
This paper introduces MLOCSR, a system that pipelines low-level image vectorization with a high-level probabilistic Markov Logic Network to recognize chemical structures. It replaces brittle heuristics with weighted logic rules, significantly outperforming state-of-the-art systems like OSRA on degraded or low-resolution images.