Best Open-Source OCR Models

Open-source OCR in 2026 is led on document-specific benchmarks by compact specialized models rather than very large general-purpose vision-language models, with PaddleOCR-VL-1.6 scoring 96.34% on OmniDocBench v1.6, followed by MinerU2.5-Pro at 95.75% and GLM-OCR at 95.22%, while substantially larger models such as Qwen3-VL-235B score lower. Dedicated systems are generally better for high-volume, cost-efficient document parsing, structured extraction, tables, formulas, layouts, and reading order, whereas general-purpose VLMs such as Qwen3.5 offer broader capabilities for reasoning about documents, charts, images, and related multimodal content. PaddleOCR-VL emphasizes multilingual document parsing, PP-OCR provides lightweight scene-text recognition, GLM-OCR targets complex structured documents, MinerU combines specialized components for PDF reconstruction, and other options including dots.mocr, Chandra OCR 2, and EasyOCR address visual graphics, handwriting, forms, or lightweight text extraction. Compact VLMs such as Florence-2 and SmolVLM2 offer a middle ground by combining OCR with visual understanding at lower hardware requirements. Model selection should consider accuracy, language coverage, document complexity, speed, deployment hardware, customization, privacy, and licensing, while Roboflow Workflows and its Agent provide low-code tools for incorporating several OCR models into computer-vision pipelines without independently managing deployment infrastructure.

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