How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and — 2026-07-24

## Short Segments ## Feature Story Today, we're diving into the practicalities of building an end-to-end OCR pipeline using Baidu's Unlimited-OCR model. This model, with its 3 billion parameters, is designed for high-resolution images and multi-page PDF parsing, offering a significant leap in document processing capabilities. The Unlimited-OCR model is a game-changer in the field of optical character recognition. It supports both single-page and multi-page document processing, utilizing a 32,768 token context window. This allows it to handle extensive documents in one go, a feat that traditional OCR models struggle with due to memory constraints. To set up this pipeline, the first step involves configuring the GPU environment and installing necessary dependencies. This includes libraries like Transformers, Pillow, and PyMuPDF, among others. The model is then loaded with automatic selection of bfloat16 or float16 precision, optimizing performance on NVIDIA GPUs. Once the environment is ready, the workflow begins with generating structured sample documents for testing. This is crucial for evaluating the model's performance in real-world scenarios. The pipeline supports two inference modes: the tiled Gundam mode for detailed single-page OCR and the faster Base mode for quicker processing. Extending the pipeline to multi-page PDF parsing is where the Unlimited-OCR truly shines. Using PyMuPDF and the infer_multi() function, the model can process dense layouts, tables, paragraphs, and cross-page content seamlessly. This is achieved while maintaining long-context generation settings and repetition controls, ensuring structured output handling. The model's ability to process over 40 pages in a single pass is made possible by a novel memory-efficient architecture. This redesign in the attention mechanism allows the model to treat memory like human forgetting, keeping memory use and speed constant regardless of text length. This capability not only challenges traditional OCR approaches but also offers faster and more accurate long-document processing. The open-sourcing of Unlimited-OCR marks a significant advance in open-source document AI, strengthening China's position in this domain. For developers and enterprises, this means more efficient document processing workflows. The ability to handle large volumes of text in a single pass reduces processing time and resource consumption, making it a cost-effective solution for businesses dealing with extensive documentation. As we look ahead, the implications of this technology are vast. From automating data entry to enhancing digital archiving, the potential applications are numerous. The open-source nature of Unlimited-OCR also invites further innovation and customization, allowing developers to tailor the model to specific needs. In conclusion, Baidu's Unlimited-OCR model represents a significant step forward in OCR technology. Its ability to process multi-page documents efficiently and accurately opens up new possibilities for document management and processing. As more organizations adopt this technology, we can expect to see a transformation in how documents are handled across various industries.