Intelligent Document Processing (IDP) is reshaping how organizations extract insights and drive actions from complex, unstructured documents across sectors like healthcare, insurance, finance, and government. Unlike traditional OCR tools that merely extract raw text, modern IDP solutions integrate Large Language Models (LLMs), vision-language understanding, and modular workflows to deliver context-aware document interpretation at scale.
This new generation of IDP technology enables organizations to automate document-heavy processes, understand handwriting and visual structures, and reduce manual intervention. The result is improved accuracy, faster processing, and significant gains in operational efficiency and compliance.
In this white paper, we present a comparative evaluation of state-of-the-art IDP technologies, including GPT-4o, Claude 3.7, Gemini 2.0, and Llama 3.2. We explore their performance across key parameters such as accuracy, latency, multimodal capability, and real-world applicability. The paper also highlights how modern LLMs, and vision-language models are enabling robust document extraction across text, images, tables, and handwriting. We discuss the role of human-in-the-loop feedback, model adaptability, and hybrid AI pipelines in advancing intelligent document automation. This guide offers essential insights for enterprises looking to adopt robust, scalable, and future-proof document processing solutions.
