Recent OCR models achieve strong results on document benchmarks, with several 1–2B OCR-specialized models surpassing much larger general multimodal LLMs (MLLMs). However, complex real-world documents often contain blur, cropping, curved text, handwriting, and unfamiliar structures, which remain challenging for current OCR systems. We trace this gap to a shift in the error source: errors on canonical benchmarks mainly arise from layout analysis, including localization, element classification, and reading order, while errors under these challenging conditions mainly arise from content recognition. To this end, we propose CL-OCR, a two-stage framework that first fuses semantic structure from an MLLM-based detector with geometric candidates from a vision detector to obtain calibrated layouts, then enhances recognition via a shared LoRA adapter gated by a lightweight router. We further construct OCRWildBench and a companion training split to study these conditions. Experiments show that CL-OCR remains competitive on canonical benchmarks and achieves the strongest overall performance among OCR-specialized models on OCRWildBench.