Fast360

Open-source arena for benchmarking OCR models on PDF-to-Markdown conversion

4.8 (5)

审阅者 Daniel Nikulshyn·更新 2026年5月

概览

Fast360 is an open-source platform positioned as the first dedicated arena for comparing OCR models, with a particular focus on converting PDF documents into clean Markdown. It lets users pit different OCR engines against each other on the same source files and inspect how each handles layout, tables, formulas, and mixed content. The project is aimed at developers, researchers, and teams building document-processing pipelines who need an objective way to choose an OCR backend. By centering on Markdown output, Fast360 reflects modern use cases such as feeding parsed documents into LLMs, RAG systems, and knowledge bases. Because the codebase is open source, users can run evaluations locally, plug in new models, and adapt the arena to their own document types and quality metrics.

主要功能

OCR model comparison arena
PDF-to-Markdown conversion pipeline
Support for multiple OCR backends
Side-by-side output evaluation
Open-source and extensible codebase
Designed for LLM and RAG ingestion

优点 & 缺点

优点

Open-source and self-hostable
Direct side-by-side OCR model comparisons
Focused on LLM-ready Markdown output
Useful for benchmarking before production

缺点

Requires technical setup to run
Niche focus on PDF-to-Markdown workflows
Quality depends on integrated models
Smaller community than mature OCR tools

评测

4.8

5 个评分的平均值。

登录以留下评测。

Carlos Mendoza

Use it every day

Honestly didn't expect to like it this much. Side-by-side output evaluation is exactly what I needed, and open-source and self-hostable. but I reach for it almost every day now and it just clicks.

Camille Laurent

Does the job

Pretty happy overall. Open-source and extensible codebase just works and focused on LLM-ready Markdown output. but no dealbreakers — I'd recommend it to a friend without hesitating.

Devin Walker

Does the job

Pretty happy overall. Open-source and extensible codebase just works and focused on LLM-ready Markdown output. Quality depends on integrated models can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Tariq Aziz

Solid for our team

We rolled this out across the team last quarter and focused on LLM-ready Markdown output. Designed for LLM and RAG ingestion fits neatly into how we already work, and oCR model comparison arena removed a step we used to do by hand. but it has held up under daily use.

Sofia Lindqvist

Compared a few options

Evaluated this against two competitors. Where it wins: oCR model comparison arena and open-source and self-hostable. Where it lags: niche focus on PDF-to-Markdown workflows. On balance the feature set — especially open-source and extensible codebase — justifies the 5 stars for our use case.