Jerry Liu：OCR 是传统/脆弱系统主导的用例的完美例子，而通过应用适量的代理智能，可以既准确又廉价地解决它。
OCR 是传统/脆弱系统主导的用例的完美例子，而通过应用适量的代理智能，可以既准确又廉价地解决它。
一个适当调优的代理 OCR 需要动态地将额外计算应用于复杂元素，审查并纠正失败，并在整个页面中创建正确的语义含义。这是一项前沿模型在成本和延迟方面过于工程化，同时在长尾复杂边缘案例中表现挣扎的任务。
看看下面 @LoganMarkewich 的这篇博客文章：
https://www.llamaindex.ai/blog/ocr-is-dead-long-live-agentic-ocr
对照原文
OCR is the perfect example of a use case that has been dominated by legacy/brittle systems, and can be solved both accurately and cheaply by applying the right amount of agentic intelligence. A properly tuned agentic OCR needs to dynamically apply extra compute to complex elements, review and correct failures, and create the right semantic meaning throughout the page. It's a task that frontier models are both overengineered for in terms of cost and latency, and struggle at over a long tail of complex edge cases. Check out this blog post by @LoganMarkewich below: https://t.co/41iPHRleIW