Towards Data Science
Jun 12
When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout
Enterprise Document Intelligence [Vol. 1 #5bis] - The same relational tables.
Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload
Enterprise Document Intelligence [Vol. 1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine.
Related stories
Towards Data Science
Sep 3
Tables in PDFs for RAG: Don’t Flatten the Grid
The article titled "Tables in PDFs for RAG: Don’t Flatten the Grid" discusses Enterprise Document Intelligence, presenting a diagnostic and five composable operations rather than a decision tree. It appears in the Vol.1 #B4 issue of the publication and was first posted on Towards Data Science.
Towards Data Science
Jul 7
A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers
Enterprise Document Intelligence [Vol. 1 #9A] - Same paper, same question as Article 1.
Towards Data Science
Jun 11
Stop Returning Flat Text from a PDF: The Relational Tables RAG Needs
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 .
Towards Data Science
Jun 10
Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality
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 .
Towards Data Science
Jul 18
Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It
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 .
Towards Data Science
Jun 20
Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All
Enterprise Document Intelligence [Vol. 1 #5sexies] - image_df tells you where every picture is.
Towards Data Science
Jun 19
Parse Scanned PDFs for RAG with EasyOCR: Free OCR Gives You Words, Not a Document
Enterprise Document Intelligence [Vol. 1 #5quinquies] - Same 1974 scanned PDF, two engines.
Towards Data Science
Jun 11
Stop Returning Flat Text from a PDF: The Relational Shape RAG Needs
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 .
Towards Data Science
Jun 14
Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG
Enterprise Document Intelligence [Vol. 1 #5quater] - The other parsers read the words on a page.
Towards Data Science
Aug 23
Parse the Folder, Not Just the PDFs: The Relational Tables RAG Needs on a Case File
The article discusses how Enterprise Document Intelligence should begin by parsing the folder structure rather than just PDFs, emphasizing that the index must reflect the case type’s requirements before any folder is accessed. It highlights that the two key questions to develop are not retrieval questions but rather focus on the relational tables needed for Retrieval-Augmented Generation (RAG) in a case file context.
arXiv AI
Jun 2
Beyond String Matching: Semantic Evaluation of PDF Table Extraction
arXiv:2603. 18652v2 Announce Type: replace-cross Abstract: Reliably extracting tables from PDFs is essential for large-scale scientific data mining and knowledge base construction, yet existing evaluation approaches rely on rule-based metrics that fail to capture semantic equivalence of table content.