Computer-Vision - Page 4

Deep learning model using Swin Transformer and Focal Loss for OCSR, achieving 98.58% accuracy on synthetic benchmarks.

Deep learning OCSR method using semantic segmentation and classification CNNs to reconstruct chemical graphs with …

Deep learning method for optical chemical structure recognition using image captioning networks trained on millions of …

An end-to-end deep learning approach using U-Net and CNN-LSTM to segment and predict chemical structures from document …

A two-phase neural network approach for recognizing handwritten heterocyclic chemical rings with ~94% accuracy.

A hybrid SVM and elastic matching approach for recognizing handwritten chemical symbols drawn on touch devices, …

Online recognition of handwritten chemical symbols using Hidden Markov Models with 11-dimensional local features, …

Two-stage CNN approach for converting molecular images to SMILES using CDDD embeddings and extensive data augmentation.

Two-level algorithm for recognizing on-line handwritten chemical expressions using structural analysis, ANNs, and string …

A hierarchical grammar-based approach for recognizing and analyzing online handwritten chemical formulas in mobile …

A two-level recognition algorithm for on-line handwritten chemical expressions using structural and syntactic features.

Comprehensive review and benchmarking of 30 years of Optical Chemical Structure Recognition (OCSR) methods and tools.