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.

Multimodal Large Language Models (MLLMs) have achieved remarkable progress but continue to struggle with geometric reasoning, primarily due to the perception bottleneck regarding fine-grained visual elements. While formal languages have aided plane geometry understanding, solid geometry which requires spatial understanding remains largely unexplored. In this paper, we address this challenge by designing a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations. We construct GDP-29K, a large-scale dataset comprising 20k plane and 9k solid geometry samples collected from diverse real-world sources, each paired with its ground-truth formal description. We propose a training paradigm combining Supervised Fine-Tuning with Reinforcement Learning via Verifiable Rewards, which effectively enforces syntactic correctness and geometric consistency. Experiments show that our approach achieves state-of-the-art parsing performance. Furthermore, we demonstrate that our parsed formal descriptions serve as a critical cognitive scaffold, significantly boosting MLLMs’ capabilities for downstream geometry reasoning tasks.