How Baidu Unlimited-OCR Works: Solving Long-Document Transcription

About a month ago, Baidu (often called the “Google of China”) introduced Unlimited-OCR, an advancement over DeepSeek OCR. The model was designed to transcribe long, multi-page documents with high accuracy while delivering fast and stable inference. Unlike conventional vision-language OCR systems, Unlimited-OCR addresses a major bottleneck in long-document transcription: the rapidly growing Key-Value (KV) cache, whose memory usage rises as more output tokens are generated. In this article, we examine how Baidu’s Unlimited-OCR improves efficiency and scalability for long-document OCR tasks. Unlike traditional OCR systems, Vision-Language Models do not read raw pixels directly. Instead, every document page is divided into a grid of small square regions known as patches. Each patch is converted into a numerical embedding and treated as a visual token, similar to how words are converted into text tokens inside a language model. As a result, an entire document page becomes a sequence of visual tokens that the transformer can process. Imagine reading a newspaper through a small window. Instead of looking at the entire page at once, you inspect one small square after another until you’ve seen the complete document. Vision models perform something very similar, except they represent every square mathematically. The number of patches…