---
license: mit
size_categories:
- 1K<n<10K
---
<div align="center">

# Neural Metrics · How good is your OCR, really?

<img src="https://img.shields.io/badge/Neural%20Metrics-document%20extraction-4F46E5?style=for-the-badge" alt="Neural Metrics" />
<img src="https://img.shields.io/badge/fork%20of-getomni--ai%2Focr--benchmark-2563EB?style=flat-square" alt="fork" />

</div>

A benchmark of real documents paired with ground-truth JSON, designed to score end-to-end accuracy rather than raw character error rate. That distinction matters: an OCR pass can be 99% correct at the character level and still get the invoice total wrong.

**We use it for:** scoring candidate OCR models on the metric that actually pays the bills - regression testing before a model swap.

> ### Attribution
> This is an **unmodified fork** of [`getomni-ai/ocr-benchmark`](https://huggingface.co/datasets/getomni-ai/ocr-benchmark), created by the [Qwen team](https://huggingface.co/Qwen).
> All weights, files and behaviour are identical to upstream — we rehost it so our experiments stay
> reproducible and version-pinned. The original license and all credit remain with the Qwen team.
> If you want the canonical dataset, please use [the original](https://huggingface.co/datasets/getomni-ai/ocr-benchmark).

---

<details>
<summary><b>Original dataset card from getomni-ai/ocr-benchmark</b> (click to expand)</summary>

# OmniAI OCR Benchmark

A comprehensive benchmark that compares OCR and data extraction capabilities of different multimodal LLMs such as gpt-4o and gemini-2.0, evaluating both text and JSON extraction accuracy.

[**Benchmark Results (Feb 2025)**](https://getomni.ai/ocr-benchmark) | [**Source Code**](https://github.com/getomni-ai/benchmark)

</details>