Build an OCR-Powered PDF Reader & Summarizer with DeepInfra (Kimi K2)

DeepInfra's guide on building an OCR-powered PDF reader and summarizer with DeepInfra's Kimi K2 model offers a comprehensive walkthrough for transforming complex PDF documents into structured, machine-readable text. The guide addresses the challenges of converting PDFs, which often contain a mix of vector text and images, by using Optical Character Recognition (OCR) with Tesseract to extract text. It then employs a Large Language Model (LLM) to clean OCR artifacts, infer document structure, reconstruct tables, and summarize content into a concise, human-readable format. The workflow involves converting PDF pages into images, running OCR to extract raw text, and using the LLM for text refinement and summarization, ultimately producing a structured JSON output and a Markdown report. The document also emphasizes the importance of preprocessing, language packs for multilingual content, and the use of specific Tesseract settings to enhance accuracy. Through this process, the guide demonstrates how to effectively handle PDFs with complex layouts, such as those containing tables, and transform them into searchable and interpretable documents.