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PDF extraction breaks retrieval-augmented generation (RAG) before the retriever ever runs. A text pass can return words in the wrong order, skip pages that are only images, flatten tables into noise, and drop figures entirely, and the chunks that reach the language model look fine until someone checks them. The practical fix is not a single “best parser.” It is to test each extraction stage separately on the documents you actually need to answer questions about.
This article explains where information goes missing, what the documented tools in this space can and cannot do, and how to compare extraction options on your own corpus. It does not report results from a specific custom pipeline, so every number below comes from the sources named in the text.
Where information is lost before retrieval
A RAG system can only answer from what ingestion preserves. Losses at this stage are easy to miss because the output is still readable text, and the failure only shows up later as a wrong or unsupported answer. Four failure points account for most of the trouble with scientific and administrative PDFs.
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Scanned pages and pages built from images have no text layer for a plain extractor to read. An extractor that returns an empty string for those pages is not reporting an error; it is reporting that it found nothing. A pipeline that only checks whether the job finished will pass that document as ingested.
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Reading order
Two-column layouts, sidebars, footnotes and captions are stored in the file in whatever order the producing software wrote them, which is often not the order a human reads them. Chunks built from that raw order can split a sentence across two unrelated paragraphs or attach a caption to the wrong figure. PyMuPDF documents extraction “in natural reading order” as a separate topic from basic text extraction, which is a signal that the two are not the same operation.
Tables
A table extracted as a stream of cell values loses its row and column relationships. A question such as “what was the threshold for category B?” may retrieve the right numbers with no way to tell which header they belong to. Tables need either structured output that keeps the grid or a textual description that preserves the relationships.
Figures
Figures carry information in charts, diagrams and images that text extraction does not read. Captions and surrounding paragraphs usually describe them, so a pipeline that stores only body text can answer questions about what a paper says about a figure while having no way to answer questions about the figure itself.
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Text extraction is not the same as OCR
Most PDF libraries treat extraction and optical character recognition (OCR) as two steps. PyMuPDF’s basic text documentation shows a plain text path using page.get_text(), and it separately tells users to apply OCR to pages whose text is image-based. The wording in that documentation is:
“If your document contains image based text content the use OCR on the page for subsequent text extraction:”
In PyMuPDF, that OCR step is performed with page.get_textpage_ocr() before the subsequent extraction. The practical consequence is that a pipeline must decide per page whether OCR is needed. Running OCR on every page adds cost and can introduce recognition errors into clean digital text; skipping it leaves scanned pages empty.
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A stage-by-stage model for inspecting extraction
Treat ingestion as a sequence of checks, each of which can fail independently. The order below follows the path a document takes through the pipeline.
- Classify each page. Determine whether it has an extractable text layer, is image-only, or is mixed. Record the count of pages in each class for every document.
- Apply OCR where required. Run OCR only on pages that need it, and keep the OCR output tagged so you can later measure its error rate separately from native text.
- Check reading order and hierarchy. Compare the extracted sequence against the rendered page for multi-column layouts, headings, footnotes and captions.
- Handle tables and figures explicitly. Confirm that each table keeps its header relationships and that each figure has a caption or description attached to it.
- Validate chunks and retrieval. Check whether each chunk is self-contained enough to answer a question, and whether the retriever returns the right chunk for questions you have written against the source.
Each stage produces its own evidence. A failure in step one cannot be fixed by a better chunker in step five.
Comparing the documented options
Three documented routes illustrate the main trade-offs. The table compares what each source states, and it leaves a cell as “not stated” where the cited source does not say.
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| Axis | PyMuPDF core (get_text() and get_textpage_ocr()) |
PyMuPDF4LLM | Adobe PDF Extract API |
|---|---|---|---|
| Where it runs | Python library in your environment | Python wrapper around PyMuPDF; hosting not stated | Cloud API |
| Scanned pages | Requires an explicit OCR call per page | Not stated in the README | Documented as handling native and scanned PDFs |
| Reading order | Documented as a separate natural-reading-order extraction topic | Combines text and tables in reading order | Includes reading-order information in output |
| Tables | Documented as a separate table-extraction topic | Tables combined into Markdown output | Documented as handling complex tables |
| Figures | Documented as a separate image-extraction topic | Not stated in the README | Documented as including figures |
| Output | Text and structured data you assemble yourself | Markdown | Structured JSON for downstream processing, or Markdown for LLM ingestion |
| Evidence available | Official documentation | Project README recommending it as a starting point for RAG | Vendor documentation of capabilities |
The sources do not include a controlled head-to-head comparison between PyMuPDF4LLM and Adobe’s API. Capability documentation tells you what a tool is designed to produce; it does not tell you how accurately it produces it on your documents. The vendor and project descriptions should be treated as starting claims to test, not as measured accuracy.
Local versus hosted processing
A local library keeps documents inside your environment and gives you full control over versions and OCR settings, but you own the work of handling tables and figures and of tuning the output. A managed API moves that work out of your code, but it introduces a dependency on a service, its availability and its terms, and it means sending document content to a third party. Confirm the current feature set, data handling and pricing in the vendor’s documentation before planning around a specific tier, since API features and commercial terms change.
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A 2026 arXiv paper, From PDF to RAG-Ready: Evaluating Document Conversion Frameworks for Domain-Specific Question Answering, provides the most direct measured comparison among the sources cited here. It used a manually curated set of 50 questions over 36 Portuguese administrative documents, covering 1,706 pages and about 492,000 words. Answers were scored with an LLM-as-judge method.
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| Configuration (as named in the paper) | Reported score |
|---|---|
| Naïve PDFLoader | 86.9% |
| Manually curated Markdown | 97.1% |
| Docling with hierarchical splitting and image descriptions | 94.1% |
These scores describe one corpus, one set of pipeline configurations and one judging method. The manually curated Markdown result reflects human preparation of the text, so it shows the ceiling that careful preparation can reach rather than the output of an automated converter. The paper also reports that metadata enrichment and hierarchy-aware chunking contributed more to accuracy than converter choice alone. That finding argues for measuring chunking and metadata alongside extraction, not for choosing a converter in isolation.
How to evaluate extraction on your own documents
Build a small evaluation set from the documents your users will ask about. Use the following checklist.
- Select a representative sample that includes native digital pages, scanned pages, multi-column layouts, dense tables and figure-heavy sections.
- Write questions that can only be answered from specific tables, figures or footnotes, not only from body text.
- Record the extracted output for each document and inspect it against the rendered page for reading order, table structure and missing content.
- Run each candidate extractor and chunking setting on the same sample, and keep the configuration identical except for the variable under test.
- Score retrieval first (is the correct chunk returned?) and answer quality second, so a generation error is not mistaken for an ingestion error.
- Repeat the evaluation when you change the extractor version, OCR engine or chunk size, because those changes can shift results.
If you are building your own pipeline
A custom pipeline is worth documenting well enough that another engineer can reproduce your results. Record the following for each run:
- The extraction library and version, and the OCR engine and settings, if any.
- The page classification rule and how many pages of each type the corpus contained.
- How tables and figures are represented in the output, and how they are linked to their captions.
- The chunking strategy, chunk size, overlap and any metadata attached to each chunk.
- The evaluation set, the scoring method and the results broken down by document type, not only in aggregate.
Without those records, a score cannot be attributed to the extractor rather than to the chunker, the retriever or the corpus.
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