← 研踪 PaperTrailarXiv:2604.23546 · v2
COMO: Closed-Loop Optical Molecule Recognition with Minimum Risk Training
首次提交 2026-04-26 13:48
版本更新 2026-11-10 16:56
雷达观测 2026-11-11 13:08
摘要
Optical chemical structure recognition (OCSR) translates molecular images into machine-readable representations such as SMILES strings or molecular graphs, but remains challenging in real-world documents due to wide variations in chemical structures, shorthand conventions, and scanning artifacts. Most existing deep learning approaches rely on teacher forcing with token-level Maximum Likelihood Estimation (MLE). This training paradigm suffers from exposure bias, as models are trained under ground-truth prefixes but must condition on their own previous predictions during inference. Moreover, token-level MLE objectives hinder the optimization towards molecular-level evaluation criteria such as chemical validity and structural similarity. Here we introduce Minimum Risk Training (MRT) to OCSR and propose COMO (Closed-loop Optical Molecule recOgnition), a framework that mitigates exposure bias by directly optimizing over molecule-level, non-differentiable objectives. Experiments on ten benchmarks, including synthetic and real-world chemical diagrams from patent and scientific literature, demonstrate that COMO substantially outperforms existing rule-based and learning-based methods with less training data, consistently achieving superior performance across both reproduced baselines and previously reported results. Ablation studies further show that MRT is architecture-agnostic, demonstrating its potential for broad application to end-to-end OCSR systems.
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v2 · 2026-11-10 16:56 · 最新观测版本
COMO: Closed-Loop Optical Molecule Recognition with Minimum Risk Training
Optical chemical structure recognition (OCSR) translates molecular images into machine-readable representations such as SMILES strings or molecular graphs, but remains challenging in real-world documents due to wide variations in chemical structures, shorthand conventions, and scanning artifacts. Most existing deep learning approaches rely on teacher forcing with token-level Maximum Likelihood Estimation (MLE). This training paradigm suffers from exposure bias, as models are trained under ground-truth prefixes but must condition on their own previous predictions during inference. Moreover, token-level MLE objectives hinder the optimization towards molecular-level evaluation criteria such as chemical validity and structural similarity. Here we introduce Minimum Risk Training (MRT) to OCSR and propose COMO (Closed-loop Optical Molecule recOgnition), a framework that mitigates exposure bias by directly optimizing over molecule-level, non-differentiable objectives. Experiments on ten benchmarks, including synthetic and real-world chemical diagrams from patent and scientific literature, demonstrate that COMO substantially outperforms existing rule-based and learning-based methods with less training data, consistently achieving superior performance across both reproduced baselines and previously reported results. Ablation studies further show that MRT is architecture-agnostic, demonstrating its potential for broad application to end-to-end OCSR systems.
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