Short answer
Scanned manuals and PDFs become usable procedural data only after layout analysis, text recognition, table and figure handling, step segmentation and checks against the page image. Skipping any stage produces text that reads plausibly but loses step order, warnings or values. Plan preparation as its own scoped work with measurable acceptance tests.
The buyer's problem
Document archives look like a quick win: thousands of pages, already written, cheap to license. Then the first extraction run returns merged columns, a torque table flattened into a paragraph, a warning box placed after the step it governs, and figure captions glued to the next section. A model trained or evaluated on that output learns the damage.
For procedural data, errors are not cosmetic. A swapped number, a lost "do not", or a reordered step changes the task. The question for a buyer is therefore not whether a supplier has PDFs, but what form they arrive in, what preparation was done, and how it was checked.
What a document looks like to a parser
A manual page mixes several element types: headings, running text, numbered lists, tables, figures with callouts, captions, headers and footers, warning blocks and page furniture. Layout analysis labels these regions before text is read. DocLayNet is a human-annotated layout dataset with 80,863 manually annotated pages in COCO format and 11 layout classes across six document categories, including manuals; 7,059 pages were double-annotated and 1,591 triple-annotated, giving 91,104 annotation instances (Pfitzmann et al., 2022).
Two uses follow. A layout model trained on diverse documents can help you segment a supplier's archive. And the dataset shows that layout labeling is a human task with measurable disagreement, which is a reason to sample-check any automated pipeline on your own documents instead of trusting published accuracy on others.
Archive conditions
| Condition | Typical content | Preparation burden |
|---|---|---|
| Born-digital PDF with text layer | Text can be extracted, reading order may be wrong | Layout and order repair |
| Scanned image, clean print | Needs OCR before anything else | OCR plus layout, error sampling |
| Scanned image, degraded or annotated | Handwriting, stamps, skew, fax artifacts | OCR with manual review; some pages unusable |
| Structured source (XML or similar) | Already divided into units with metadata | Mapping rather than recovery |
The last row is the best case. S1000D, for example, is an XML-based specification in which data modules are stand-alone units of descriptive, procedural or operational data stored in a common source database and identified by a unique data module code (S1000D). A supplier who can export from the structured source skips most recovery work; see structured authoring exports.
Pipeline stages and what to verify at each
- Image quality. Resolution, skew, bleed-through and compression. Reject or rescan pages that fall below a threshold you set.
- Layout analysis. Region types and reading order. Check multi-column pages and sidebars.
- Text recognition. Character accuracy on a sample, with extra attention to digits, units and symbols such as "±" and "°".
- Tables. Preserve rows and columns; a parameter table converted to prose is not recoverable.
- Figures and callouts. Keep figure-to-step links; see illustrated assembly manuals for image-text pairing.
- Step segmentation. Number, order and nesting of steps, with warnings attached to the steps they govern.
- Schema mapping. Output to a step schema; see procedure step schema.
- Validation. Spot-check against page images, and review any LLM-assisted extraction using validating LLM-extracted steps.
Illustrative delivery specification
# Illustrative only - synthetic example, not an available dataset
delivery: parsed_manuals_v0
inputs: scanned and born-digital PDFs from one operator (mixed vintage)
per_page_outputs:
image: png (original resolution retained)
layout: regions with class, bbox, reading_order
text: per-region text with ocr_confidence
flags: [low_confidence, handwriting, stamp, table_detected]
per_document_outputs:
steps: list of {step_id, text, warnings[], figure_refs[], table_refs[]}
tables: cell grid as csv + page bbox
language: en
quality_report:
sampled_pages: reviewer-selected
char_error_rate_digits: reported separately from letters
step_order_errors: counted by human reviewer
unprocessed_pages: listed with reason
Report digit accuracy separately. A pipeline that is accurate on words can still misread "0.8" as "8" or "1" as "l".
Acceptance criteria you set
- Traceability. Every extracted step carries document, page and region identifiers so reviewers can open the source image.
- Table fidelity. A sample of tables is rebuilt by hand and compared cell by cell.
- Warning attachment. Warnings, cautions and notes map to the right steps.
- Honest failure. Unprocessable pages are listed, not silently dropped.
- Versioning. Manual edition and revision are recorded; see product user guides as instruction data for edition issues.
- Language handling. Mixed-language pages are tagged; see multilingual SOP corpora.
Who does this work
Preparation may be done by the data owner, by a preparation vendor, or by the buyer's own team. Each option shifts cost, control and rights. If a vendor processes the documents, ask about confidentiality terms for the originals and the derived text. Derived structured text can fall under the same restrictions as the source, so a license should cover derivatives explicitly. SourceX's role is sourcing, rights review, licensing and delivery coordination; annotation and preparation work are scoped case by case.
Limitations
Even perfect text extraction yields words, not skills. Parsed manuals support instruction grounding, task decomposition and question answering; they do not contain motion, force or perception data, and procedures may differ from actual practice (see procedural drift). Manuals also mix other people's content: manufacturer specifications, standards excerpts and images may carry third-party rights. Handwritten annotations on scans can contain names or sensitive notes.
Machine extraction also tends to perform better on layouts similar to those it was built or trained on. Rare formats, vertical text, dense schematics and stamped revisions break assumptions, and a pipeline's global accuracy figure will not tell you which pages those are.
Questions to ask suppliers
- What proportion of pages is born-digital, scanned clean, or scanned with handwriting or stamps?
- Is a structured source (XML, authoring tool database) available instead of PDFs?
- Which OCR and layout tools were used, and was output reviewed by people?
- Can you see page-level confidence and a list of excluded pages?
- How are tables, figures and warnings represented in the output?
- Does the license cover derived text, tables and layout labels?
- Are original scans delivered alongside text so you can re-run your own pipeline?
- Which manuals include third-party or manufacturer content?
Discuss your requirements
If you need document archives prepared for model use, describe the document types, languages and permitted uses to SourceX. SourceX looks for US businesses that hold suitable documents, checks the data and the supplier's licensing permissions, and coordinates the license and delivery. A request does not guarantee a match; SourceX follows up about potential sources.
Start from the humanoid training data guide or the procedural data hub. For general document use cases, see document AI and RAG and enterprise document archives.
Discuss your humanoid training-data requirements with SourceX
Sources
- DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis — DocLayNet provides 80,863 manually annotated pages with 11 layout classes across six document categories including manuals.
- S1000D International specification for technical publications using a common source database — S1000D structures technical publications as data modules in a common source database, a contrast to scanned page images.