TL;DR: The rise of powerful vision-language models has transformed document AI. Each model comes with unique strengths, making it tricky to choose the right one. Open-weight models offer better cost efficiency and privacy. To help you get started with them, we’ve put together this guide.
By the end, you’ll know how to choose the right OCR model, start building with it, and gain deeper insights into document AI. Let’s go!
Optical Character Recognition (OCR) is one of the earliest and longest running challenges in computer vision. Many of AI’s first practical applications focused on turning printed text into digital form.
With the surge of vision-language models (VLMs), OCR has advanced significantly. Recently, many OCR models have been developed by fine-tuning existing VLMs. But today’s capabilities extend far beyond OCR: you can retrieve documents by query or answer questions about them directly. Thanks to stronger vision features, these models can also handle low-quality scans, interpret complex elements like tables, charts, and images, and fuse text with visuals to answer open-ended questions across documents.
Recent models transcribe texts into a machine-readable format.
The input can include:
OCR models convert them into machine-readable text that comes in many different formats like HTML, Markdown and more.
Some models know where images are inside the document, extract their coordinates, and insert them appropriately between texts. Other models generate captions for images and insert them where they appear. This is especially useful if you are feeding the machine-readable output into an LLM. Example models are OlmOCR by AllenAI , or PaddleOCR-VL by PaddlePaddle .
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