Agnibina Filetype.pdf Apr 2026

Requirements (install via pip): pip install pdfplumber pymupdf tqdm tabula-py ocrmypdf # tabula-py needs Java; ocrmypdf needs Tesseract + poppler

# ------------------- Bookmarks / Outline ------------------- # def extract_bookmarks(pdf_path: Path, out_dir: Path): """Export the PDF's outline (bookmarks) as a JSON hierarchy.""" doc = fitz.open(str(pdf_path)) toc = doc.get_toc(simple=False) # list of [level, title, page, ...] # Turn into a nested dict for readability def build_tree(toc_entries): tree = [] stack = [(0, tree)] # (level, container) for level, title, page, *_ in toc_entries: while level <= stack[-1][0]: stack.pop() node = "title": title, "page": page, "children": [] stack[-1][1].append(node) stack.append((level, node["children"])) return tree

safe_mkdir(out_dir / "tables") # tabula can auto-detect tables across the whole doc: tables = tabula.read_pdf(str(pdf_path), pages="all", multiple_tables=True, pandas_options='dtype': str) print(f"📊 Detected len(tables) tables.") for i, df in enumerate(tables, start=1): # Try to infer the page number from the DataFrame's metadata if present # (tabula doesn’t expose page number directly; you can run per-page if you need it) csv_path = out_dir / f"tables/table_i:03d.csv" df.to_csv(csv_path, index=False) print(f" → Saved table i → csv_path") agnibina filetype.pdf

# Optionally re-run the extraction on the OCR’d file # (You could replace the original path with ocr_output for downstream steps)

def clean_filename(s: str) -> str: """Make a filesystem‑safe name.""" return re.sub(r"[^\w\-_. ]", "_", s) | Category | Typical Features | Why they’re

# ------------------- Main driver ------------------- # def main(): parser = argparse.ArgumentParser( description="Extract a suite of features from a PDF (e.g. agnibina.pdf)." ) parser.add_argument("pdf", type=Path, help="Path to the input PDF") parser.add_argument( "-o", "--out

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 | number of pages

# ------------------- Metadata ------------------- # def extract_metadata(pdf_path: Path) -> Dict: """Return a dict with PDF metadata (title, author, dates, etc.).""" doc = fitz.open(str(pdf_path)) meta = doc.metadata # Normalize keys normalized = "title": meta.get("title"), "author": meta.get("author"), "creator": meta.get("creator"), "producer": meta.get("producer"), "subject": meta.get("subject"), "keywords": meta.get("keywords"), "creationDate": meta.get("creationDate"), "modDate": meta.get("modDate"), "pdf_version": doc.pdf_version, "page_count": doc.page_count, doc.close() return normalized

count = 0 for i in range(doc.embfile_count()): info = doc.embfile_info(i) fname = clean_filename(info["filename"]) data = doc.embfile_get(i) (att_dir / fname).write_bytes(data) count += 1 doc.close() print(f"📦 Extracted count embedded file(s).")