How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and Multi-Page PDF Parsing
In this tutorial, we build a complete workflow for running Baidu’s Unlimited-OCR model on document images and multi-page PDFs. We configure the GPU environment, install the required dependencies, load the 3B-parameter vision-language model with automatic selection of bfloat16 or float16, and generate structured sample documents for testing. We then evaluate both the tiled Gundam inference mode and the faster Base mode for single-page OCR before extending the pipeline to multi-page PDF parsing w
This tutorial demonstrates how to build an optical character recognition (OCR) pipeline that processes high-resolution document images and multi-page PDFs using a vision-language model. The pipeline involves configuring a GPU environment, loading a 3-billion-parameter model, and then running the model in two different inference modes: a tiled "Gundam" mode for detailed recognition and a faster "Base" mode for simpler layouts. The workflow preserves settings for long text generation and structured output handling to process dense layouts, tables, paragraphs, and content that spans across multiple pages in a single end-to-end pipeline.
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Also reported by The Verge, SiliconANGLE and MarkTechPost
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Also reported by SiliconANGLE
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