OCRBench v2

OCRBench v2

Quick answer: OCRBench v2 (Fu et al., 2025) is a large-scale, bilingual (English/Chinese) text-centric benchmark with roughly 4x more tasks than the original OCRBench, covering 31 diverse scenarios and 10,000 human-verified question-answer pairs — including text localization, handwritten content extraction, and logical reasoning over text-rich images. North Micro Vision scores 36.7% on the English subset as of August 2026.

At a Glance

What it tests: Bilingual OCR capability across 31 scenarios and 10,000 human-verified questions, emphasizing harder sub-tasks than plain text recognition — text localization, handwriting extraction, fine-grained/layout perception, complex element parsing, and logical reasoning.

Why it matters: OCRBench v2's authors found most large multimodal models score below 50/100 on its harder task mix, showing that OCR capability claimed via simpler recognition benchmarks doesn't necessarily transfer to localization- and reasoning-heavy real-world OCR tasks.

Known limitations: As a newer, deliberately harder benchmark, current absolute scores are still low across the board — most models, including several frontier systems, score below 50%, so results should be read as early-stage progress rather than near-ceiling performance.

What OCRBench v2 Measures

OCRBench v2 substantially expands on the original OCRBench, offering roughly 4x more task types across 31 diverse real-world scenarios, with 10,000 human-verified question-answer pairs plus a held-out private test set of 1,500 manually annotated images used to validate result consistency. Task types go well beyond plain text recognition to include precise text localization (bounding a specific piece of text within an image), handwritten content extraction, fine-grained and layout perception, complex multi-element parsing, and logical reasoning over text-rich content.

The paper's central finding is that most large multimodal models — including many considered strong OCR performers on the original OCRBench — score below 50 out of 100 on this harder task mix, and share five common limitation categories: rare/uncommon text recognition, fine-grained perception, layout perception, complex element parsing, and logical reasoning.

Because of this difficulty gap, OCRBench v2 is currently the more discriminative and future-proof OCR benchmark of the two, while the original OCRBench remains useful mainly as a simpler, faster baseline check.

Benchmark Specifications

How OCRBench v2 Is Scored

Each of the 10,000 questions is scored for correctness across the benchmark's task categories (localization, extraction, layout perception, complex parsing, and reasoning); the overall score is the percent accuracy across the full public English or Chinese test subset, with a private held-out set used to validate result reliability.

State-of-the-Art Results

Score sourced from Cohere Labs' North Micro Vision Instruct launch announcement, August 2026, evaluated on the English (v2_en) subset.

OCRBench v2 on Benchgen

No Benchgen results yet — be the first to run OCRBench v2.

OCRBench v2 vs Other Benchmarks

OCRBench v2 is the harder, more comprehensive successor to OCRBench — use it when comparing frontier or near-frontier models where the original OCRBench no longer discriminates well.

Run OCRBench v2 on Your Model

Benchgen lets you evaluate your own vision-language model's OCR capability across text localization, handwriting extraction, and logical reasoning, and track results across model versions.

Related Models & Benchmarks

• Top performers: North Micro Vision Instruct
• Related benchmarks: OCRBench · InfoVQA

Frequently Asked Questions