Top 4 Open-Source OCR Models

Algo Insights

4 min read

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Oct 26, 2025

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I’ve spent a lot of time lately testing different OCR models — trying to find one that truly reads PDFs, scans, and screenshots the way humans do.
Four open-source projects really stand out this year: DeepSeek-OCR, Olmo-OCR 2, Qwen3-VL, and Dots.OCR.

Each of them does the same thing (read documents) but in completely different ways. Here’s what I’ve learned from working with them.

1. DeepSeek-OCR-3B — The Speed Demon

If you want a fast, reliable OCR model that can chew through images and PDFs without drama, DeepSeek-OCR is it.

It runs on GPUs using either vLLM or Transformers, so you can pick whichever fits your setup.

Setting it up takes a few commands:

git clone https://github.com/deepseek-ai/DeepSeek-OCR.git
cd DeepSeek-OCR
conda create -n deepseek-ocr python=3.12.9 -y
conda activate deepseek-ocr
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
pip install flash-attn==2.7.3 --no-build-isolation

Once you’re ready, here’s a quick Python snippet to try:

from vllm import LLM, SamplingParams
from PIL import Image

llm = LLM(model="deepseek-ai/DeepSeek-OCR")
img = Image.open("your_image.png").convert("RGB")
out = llm.generate([{"prompt": "<image>\nRead this.", "multi_modal_data": {"image": img}}]…