Principle:Unstructured IO Unstructured PDF Partitioning
| Knowledge Sources | |
|---|---|
| Domains | Document_Processing, PDF, Computer_Vision |
| Last Updated | 2026-02-12 00:00 GMT |
Overview
A specialized partitioning process that extracts structured elements from PDF documents using format-specific strategies including text extraction, layout detection, and OCR.
Description
PDF partitioning handles the most complex document format in unstructured data processing. PDFs can be born-digital (with embedded text), scanned (image-only), or mixed. The PDF partitioner supports multiple extraction strategies:
- Fast mode: Uses pdfminer.six to extract embedded text with layout heuristics. Fastest but cannot handle scanned content.
- Hi-res mode: Renders pages to images, runs a layout detection model (YOLOX/Detectron2) to identify regions, then applies OCR or text extraction per region.
- OCR-only mode: Renders pages to images and applies Tesseract OCR to extract all text.
Additionally, PDF partitioning supports table structure inference (converting detected table regions to HTML), image extraction, form extraction, and fine-grained pdfminer tuning parameters.
Usage
Use this principle when processing PDF documents that require format-specific control beyond what the generic partition() function exposes. This includes tuning pdfminer margins, extracting forms, handling password-protected PDFs, or controlling table structure inference independently from the general pipeline.
Theoretical Basis
PDF processing combines several techniques:
Text extraction (pdfminer): PDFs store text as positioned character sequences with font information. The pdfminer library reconstructs reading order using configurable margin parameters: line_margin (vertical distance to group characters into lines), char_margin (horizontal distance to group characters into words), word_margin (distance between words), and line_overlap (overlap tolerance for same-line detection).
Layout detection (hi_res): Page images are passed through object detection models trained on document layouts. These models predict bounding boxes with class labels (title, text, table, figure, list) and confidence scores. Post-processing groups boxes into an ordered element sequence.
Table structure recognition: Detected table regions are analyzed to identify row/column structure and cell contents, producing HTML representations (text_as_html in metadata).