I’ll walk through the typical kinds of features you might want, the tools that can get them, and a ready‑to‑run Python snippet (plus a few command‑line alternatives) so you can start extracting right away. | Category | Typical Features | Why they’re useful | |----------|------------------|--------------------| | Metadata | Title, author, creation/modification dates, producer, PDF version, number of pages, subject, keywords | Quick bibliographic info; helps with indexing, deduplication, compliance | | Structural | Table of contents, headings hierarchy, page numbers, bookmarks, sections, paragraph breaks | Re‑creates the document outline; useful for navigation, summarisation, or building a search index | | Textual | Full‑text extraction, word‑frequency counts, named entities (people/places/orgs), key phrases, language detection | Core content for search, NLP, summarisation, sentiment analysis | | Layout | Location (x, y coordinates) of each text block, fonts, font sizes, colors, line spacing | Enables reconstruction of the original layout, detecting headings, footnotes, captions | | Tabular | All tables (cell‑by‑cell data), table captions, table bounding boxes | Essential for data mining, financial reports, scientific results | | Visual | Embedded images (raster & vector), image captions, image dimensions, DPI, color model | For image‑based analysis, OCR, checking for diagrams, extracting figures | | Annotations | Highlights, comments, sticky notes, form fields, signatures | Useful for reviewing workflows, compliance checks | | Embedded Files | Attachments, embedded spreadsheets, PDFs, ZIPs | May contain supplemental data | | OCR (if scanned) | Recognised text from images, confidence scores | Turns a scanned PDF into searchable text |
# ------------------- Tables ------------------- # def extract_tables(pdf_path: Path, out_dir: Path): """ Uses tabula-py (Java) to pull out tables. Each table is saved as CSV under out_dir/tables/page_XX_table_YY.csv . """ try: import tabula except ImportError: print("⚠️ tabula-py not installed – skipping table extraction.") return agnibina filetype.pdf
Features covered: * Basic metadata * Full text (with page numbers) * Text layout (coordinates, fonts) * Images (saved to disk) * Tables (as CSV) * Bookmarks / outline * Embedded files (attachments) * Optional OCR for scanned PDFs I’ll walk through the typical kinds of features
import pdfplumber import fitz # pymupdf from tqdm import tqdm the tools that can get them
# Optionally re-run the extraction on the OCR’d file # (You could replace the original path with ocr_output for downstream steps)
Requirements (install via pip): pip install pdfplumber pymupdf tqdm tabula-py ocrmypdf # tabula-py needs Java; ocrmypdf needs Tesseract + poppler
If you only need a subset, simply comment out the relevant blocks. """