#document-ai

3 articles

01 ·RAG & Retrieval·★ MEMBER·PAPER·7 min read Paper Explained: D-RAC — Normalize Everything to PDF, Read the Pages Once, Then Chunk by ID D-RAC ingests PDFs, Word files and slides for RAG by rendering everything to PDF, converting the pages to Markdown with one multimodal LLM pass, and then letting an LLM plan chunks using only element IDs. Explained from scratch. 02 ·★ MEMBER·PAPER·9 min read Paper Walkthrough: The Design Fundamentals of Pixel Text Representation Learning An encoder that reads meaning straight off the pixels, never converting glyphs to character codes. This EMNLP 2026 paper argues that what decides its quality is not data volume but four design choices — explained from zero. 03 ·VLMs & Multimodal·★ MEMBER·PAPER·10 min read Document AI and OCR Today — How an LLM Ends Up Reading Your Invoices How machines came to read invoices and scanned PDFs, from first principles: text detection and CTC, how errors are measured (CER), LayoutLM's trick of embedding coordinates alongside words, the OCR-free Donut line, and today's habit of handing the page straight to a VLM — plus the walls that matter in production: tables, handwriting, and hallucination.