FireRed-OCR is a specialized framework developed by FireRedTeam to transform general Large Vision-Language Models (LVLMs) into high-performance, pixel-precise structural document parsing experts. It addresses the issue of "Structural Hallucination" in general VLMs by shifting the paradigm from "impressionist" text generation to "structural engineering," achieving state-of-the-art results on benchmarks like OmniDocBench v1.5.
SOTA Performance: Achieves 92.94% overall score on OmniDocBench v1.5, outperforming models like DeepSeek-OCR 2 and OCRVerse.
Structural Integrity: Utilizes Format-Constrained GRPO (Group Relative Policy Optimization) to enforce strict syntactic validity, eliminating common errors like unclosed tables or invalid LaTeX formulas.
"Geometry + Semantics" Data Factory: Employs a novel data engine that uses geometric feature clustering and multi-dimensional tagging to synthesize balanced datasets, effectively handling long-tail layouts.