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OCR Benchmarks 2025: The Best Open Source Models in a Practical Test

After we explained in the first part of our series how LLM-based OCR fundamentally differs from classic methods, and in the second part examined the technical implementation, we now turn to the crucial question of model selection.. The market for open-source models is moving rapidly, and the choice of the right "engine" significantly determines the quality and efficiency of the pipeline.

OCR for Sensitive Data on Your Own GPU

In this second part, we focus on the practical implementation of this high-performance pipeline. We show step-by-step how to set up a dedicated, fast processing server on your own NVIDIA GPU using Podman (on Rocky Linux) and the vLLM inference engine. We then build an asynchronous Python client to fully leverage the GPU's power and process even large stacks of documents.

How LLMs Are Revolutionizing OCR-Based Document Analysis

In this first part we look at the conceptual advantages of Large Language Models (LLMs) in document analysis. The technical implementation and practical code examples of the two contrasting pipelines will follow in detail in an accompanying article.