Dual-Path Global Awareness Transformer for Optical Chemical Structure Recognition

Optical chemical structure recognition (OCSR), reconstructing structural information from chemical graphics into machine-readable sequences, has recently emerged as a focal research topic at the intersection of materials science and computer science. Existing multimodal fusion methods possess limited awareness of global context and often produce erroneous sequences when encountering complex motifs, such as rings or long chains. To address this issue, we propose the dual-path global awareness transformer (DGAT) for sequence generation. Its cascaded global feature enhancement (CGFE) module emphasizes global context and bridges cross-modal gaps, while the sparse differential global-local attention (SDGLA) module dynamically captures fine-grained differences between global and local features. We have constructed a new evaluation data set, and DGAT achieves state-of-the-art (SOTA) performance, reaching BLEU-4 = 0.840 (+5.3%), ROUGE-L = 0.908 (+1.9%), and mean Tanimoto similarity = 0.988 (+1.2%) over the best published model, confirming its ability to generate sequences that are both symbolically accurate and chemically precise.