Enterprise Document Intelligence [Vol. 1 #5A] - Document signals (metadata, native TOC, source software) and page-level content (text vs scans, tables, images, columns, page profile) The post Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality appeared first on Towards Data Science .
By Kezhan Shi
Enterprise Document Intelligence [Vol. 1 #5B] - One PDF in, a relational set of DataFrames out: lines, pages, TOC, images, cross-references, captions, spans, and a parsing summary The post Stop Returning Flat Text from a PDF: The Relational Shape RAG Needs appeared first on Towards Data Science .
By Kezhan Shi
Enterprise Document Intelligence [Vol. 1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine.
By Kezhan Shi
Enterprise Document Intelligence [Vol. 1 #5B] - One PDF in, a relational set of DataFrames out: lines, pages, TOC, images, cross-references, captions, spans, and a parsing summary The post Stop Returning Flat Text from a PDF: The Relational Tables RAG Needs appeared first on Towards Data Science .
By Kezhan Shi
Enterprise Document Intelligence [Vol. 1 #7A] - Stop searching strings.
By angela shi
Enterprise Document Intelligence [Vol. 1 #4] - A diagnostic across PDFs and questions, and a map of the techniques the rest of the series will cover The post From Regex to Vision Models: Which RAG Technique Fits Which Problem appeared first on Towards Data Science .
By angela shi
Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted.
By angela shi
Enterprise Document Intelligence [Vol. 1 #5quater] - The other parsers read the words on a page.
By Kezhan Shi
Enterprise Document Intelligence [Vol. 1 #5quinquies] - Same 1974 scanned PDF, two engines.
By Kezhan Shi
Enterprise Document Intelligence [Vol. 1 #9A] - Same paper, same question as Article 1.
By angela shi
Enterprise Document Intelligence [Vol. 1 #10A] - The escalation cascade and the free, deterministic checks that flag a failed parse before you pay for a deeper one The post Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It appeared first on Towards Data Science .
By angela shi
Testing fourteen engines on ninety-three human documents The post I Spent May Evaluating Different Engines for OCR appeared first on Towards Data Science .
By Ida Silfverskiöld