Parsing PDFs and Messy Documents
Get clean, well ordered text, tables and page numbers out of PDFs, scans, slides and office files.
A taste of a lesson
Text from our two column reports comes out with sentences mixed between columns. What can I do?
That is a reading order problem: basic extractors often read straight across the page, joining lines from both columns. Try a layout aware parser that detects columns and outputs text column by column, ideally with headings preserved. For pages it still gets wrong, a vision capable model can transcribe the page in reading order, at higher cost. Then add a check, such as sampling a few pages per report and reading them. How many reports do you have, and are they all the same layout?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain why PDFs are hard to extract and tell digital files from scans
- Choose between basic extraction, layout parsers, OCR and vision models
- Detect broken extractions with simple automatic checks
- Keep page numbers, headings and tables usable for retrieval and citations
- Handle slides, office documents, web pages and spreadsheets sensibly
Lesson plan
- 1 Why PDFs are difficult Understand that PDFs store layout, not document structure. Start
- 2 Common extraction problems Recognise the usual ways extraction goes wrong. Start
- 3 Tool categories and routing Pick extraction approaches by file and page type. Start
- 4 Automatic quality checks Catch broken extractions across many files without reading them all. Start
- 5 Tables, pages and headings Preserve the structure that retrieval and citations need. Start
- 6 Other formats Handle office files, slides, web pages and spreadsheets appropriately. Start
Try asking
About this tutor
For beginners preparing documents for search or RAG who discover that the text coming out of their PDFs is jumbled, empty or full of repeated headers. You learn why PDFs are hard (they store layout, not structure), how to tell digital PDFs from scans that need OCR, and which categories of tools handle text extraction, layout analysis, OCR and difficult pages with vision capable models. You practise quick quality checks that catch broken extractions early, keep page numbers for citations, and handle tables, multi column layouts and other formats such as slides, spreadsheets and web pages.
Reviews
4.5
2 ratingsSample
- Ingrid N.Sample
The characters per page check found dozens of scanned files we thought were digital. Our retrieval had been silently missing them.
- Abdul R.Sample
Clear explanation of why PDFs are hard. Routing only the bad pages to the expensive method kept our costs down.
About the teacher
Takes retrieval systems from demo to dependable: parsing, citations, freshness, retrieval evaluation and debugging
9 tutors 267 lessons taught Sample
Most RAG demos work on the ten documents someone picked. I teach what happens after that: scanned PDFs, tables, documents that change every week, answers that cite the wrong page and users who ask things the documents never covered. My background is in document processing and internal knowledge tools, so I am practical about formats and sceptical of any setup...
See Fumiko's profile and tutorsMore like this
Other tutors on the same or nearby topics.