How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and Multi-Page PDF Parsing
MarkTechPost Sana Hassan
A tutorial demonstrates how to build an OCR pipeline using Baidu's Unlimited-OCR, a 3-billion-parameter vision-language model, for processing document images and multi-page PDFs in Google Colab. The model is loaded with automatic dtype selection (bfloat16 or float16) and tested in two inference modes: Gundam mode with tiled crops for dense text and Base mode with single full-page views for faster processing. The workflow extends to multi-page PDF parsing using PyMuPDF and infer_multi(), preserving long-context generation settings to handle tables, paragraphs, and cross-page content in a single end-to-end pipeline.
Why it matters
In this tutorial, we build a complete workflow for running Baidu’s Unlimited-OCR model on document images and multi-page PDFs. From configuring the GPU environment to comparing high-detail tiled Gundam inference and faster Base modes, you'll learn how to process dense layouts, tables, and cross-page content in a reproducible, end-to-end pipeline. The post How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and Multi-Page PDF Parsing appeared first on MarkTechPost.
