Identifying the Best OCR API: Benchmarking OCR APIs on Real-World Documents

The text discusses the evolving landscape of text extraction technologies, comparing Large Language Models (LLMs) and Optical Character Recognition (OCR). Despite advancements in LLMs and Vision-Language Models (VLMs), OCR remains crucial for applications requiring high accuracy, such as financial records and legal documents, due to its reliability and efficiency on low-power devices. OCR's consistency in structured output and the provision of confidence scores make it preferable over LLMs, which can produce hallucinations and lack reliability. A benchmark of various OCR APIs, including commercial solutions like Google Cloud Vision AI and open-source models like PaddleOCR, evaluates them on metrics such as accuracy, latency, and cost. Google Cloud Vision AI emerges as a top performer in accuracy, while Azure AI Document Intelligence proves cost-effective. The study concludes that OCR is indispensable for precise text extraction, complementing LLMs in a combined approach for enhanced document processing and interpretation.