While Large Language Models (LLMs) have revolutionized scientific text processing, they exhibit a significant capability gap when interpreting chemical reaction diagrams. We identify two bottlenecks restricting current systems: a Visual Deficit, where generic vision encoders struggle to resolve the strict topological connectivity of dense molecular graphs, and a Semantic Disconnect, where standard linear strings (e.g., SMILES), although structurally precise, are often poorly aligned with the chemical semantic abstractions used by LLMs for reasoning. To bridge these gaps, we propose the Chemical Visual Activation (ChemVA) framework, which employs a novel Visual Anchor mechanism to ground functional groups via hybrid-granularity detection, followed by a semantic alignment approach that translates recognized structures into language-accessible chemical representations for downstream LLM reasoning. We evaluate our approach on OCRD-Bench, a newly constructed benchmark spanning recognition, chemical knowledge, and mechanistic reasoning. Experiments show that ChemVA achieves 92.0% structural recognition accuracy. It further delivers a consistent performance gain of approximately 20 percentage points across 9 diverse LLMs, allowing open-weights models to approach strong proprietary systems on complex chemical reasoning tasks.