Agnibina Filetype.pdf Official

# Quick heuristic: count characters on first page with pdfplumber.open(str(pdf_path)) as pdf: first_page_text = pdf.pages[0].extract_text() if first_page_text and len(first_page_text.strip()) > 30 and not force: print("✅ PDF already contains text – OCR not required.") return

# ------------------- Embedded Files ------------------- # def extract_attachments(pdf_path: Path, out_dir: Path): """Save any attached files (PDF attachments, ZIPs, etc.) to out_dir/attachments/.""" doc = fitz.open(str(pdf_path)) att_dir = out_dir / "attachments" safe_mkdir(att_dir) agnibina filetype.pdf

def clean_filename(s: str) -> str: """Make a filesystem‑safe name.""" return re.sub(r"[^\w\-_. ]", "_", s) # Quick heuristic: count characters on first page

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 | the tools that can get them

# ------------------- Helper functions ------------------- # def safe_mkdir(p: Path): p.mkdir(parents=True, exist_ok=True)

outline = build_tree(toc) (out_dir / "bookmarks.json").write_text(json.dumps(outline, indent=2, ensure_ascii=False)) doc.close() print(f"🔖 Extracted len(toc) outline entries.")

img_counter = 0 for page_num in tqdm(range(len(doc)), desc="Pages (images)"): page = doc[page_num] img_list = page.get_images(full=True) for img_index, img in enumerate(img_list, start=1): xref = img[0] base_image = doc.extract_image(xref) img_bytes = base_image["image"] img_ext = base_image["ext"] img_name = f"pagepage_num+1:03d_imgimg_index:03d.img_ext" (img_dir / img_name).write_bytes(img_bytes) img_counter += 1 doc.close() print(f"✅ Extracted img_counter images to img_dir")