mlsanityData to 14 Oct 2026

MolParser-Mobile: Ultrafast OCSR System for Large-Scale Chemical Literature Mining

arXiv 2609.05807 ↗First posted 5 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

Optical Chemical Structure Recognition (OCSR) is a fundamental component of chemical literature mining, enabling molecular database construction, reaction extraction, and AI-driven scientific discovery. Despite substantial progress in recognition accuracy with recent deep learning-based methods, inference throughput remains a critical bottleneck that limits web-scale deployment. To address this challenge, we propose MolParser-Mobile, an AutoML-optimized lightweight end-to-end OCSR framework. MolParser-Mobile contains only 9.98M parameters, while reaching a throughput of 1,520 molecules per second on a single NVIDIA RTX 4090D GPU. Despite its compact design, it maintains competitive and, on several benchmarks, superior recognition accuracy.

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References · 10

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  3. 2021How to train your ViT? Data, Augmentation, and Regularization in Vision Transformerscited by 569
  4. 2022MolScribe: Robust Molecular Structure Recognition with Image-To-Graph Generationcited by 49
  5. 2023MolGrapher: Graph-based Visual Recognition of Chemical Structurescited by 24
  6. 2024MolParser: End-to-end Visual Recognition of Molecule Structures in the Wildcited by 17
  7. 2024MolNexTR: A Generalized Deep Learning Model for Molecular Image Recognitioncited by 16
  8. 2022MolMiner: You only look once for chemical structure recognitioncited by 10
  9. 2025Uni-Parser Technical Reportcited by 3
  10. 2026BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literaturecited by 1

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