Multilingual-pdf2text Access

(e.g., pdfminer.six , pdf.js , PyMuPDF ). This extracts text runs with their exact positions, font names, and Unicode mappings. The core challenge here is mapping PDF’s ad-hoc encoding to Unicode . Many PDFs use custom or non-embedded encodings (e.g., MacRoman, WinAnsi, or a bespoke 8-bit mapping). Without ToUnicode tables, the engine must guess character mappings—a frequent source of mojibake in older or Eastern European documents.

Until extractors treat Devanagari, Arabic, and Latin as equal citizens rather than Latin + exceptions, the Babel pipeline will remain incomplete. The final step is not better code. It is recognizing that a page of text is not a rectangle to be scanned, but a cultural artifact to be translated—in the deepest sense of the word. : ~1,850 Total with headings : ~2,100 multilingual-pdf2text

(heuristics + ML). PDFs lack a DOM tree. Text blocks must be clustered by Y-coordinates (lines), then X-coordinates (words), then sorted. For Latin, a simple top-to-bottom, left-to-right rule works 80% of the time. But for Mongolian (vertical), traditional Japanese (top-to-bottom, right-to-left columns), or mixed scripts (Arabic text with Latin numbers), static heuristics fail. Modern systems (e.g., Adobe’s Extract API, Google’s DocAI) use layout-aware transformers (LayoutLM, Donut) trained on millions of document pages to infer logical spans. Many PDFs use custom or non-embedded encodings (e

1. Introduction: The Document as a Lie The Portable Document Format (PDF) is a masterpiece of fidelity and a nightmare of accessibility. Designed by Adobe in 1993 to preserve exact visual layouts across disparate systems, the PDF prioritizes geometric precision over semantic flow. To a computer, a PDF is not a sequence of words or paragraphs; it is a collection of drawing commands: moveto , lineto , show . Text is not a string but a set of glyphs placed at absolute coordinates. The final step is not better code

(ICU, HarfBuzz). For complex scripts (Devanagari, Thai, Arabic), PDFs may store precomposed glyphs (e.g., क + ् + त → क्त) or store them as separate components that must be re-ordered and ligated. A multilingual engine must reverse the shaping process. For Arabic, it must detect the base character from initial/medial/final glyph forms. For Tamil, it must reorder vowel signs that appear left or right of the consonant in print but must follow the consonant in logical Unicode.

# Conceptual pipeline (pseudo-code) class MultilingualPDFExtractor: def extract(self, path): # Stage 0: Render to image + text layer images = pdf2images(path, dpi=150) raw_textruns = pdfminer_extract(path) # Stage 1: Glyph-to-character (HarfBuzz shaping) char_sequence = harfbuzz_shape(raw_textruns, font=extract_fonts(path)) # Stage 2: Reading order (detect columns / vertical text) blocks = cluster_by_position(char_sequence) ordered = resolve_reading_order(blocks) # ML or heuristic # Stage 3: Language ID per block (CLD3) for block in ordered: lang, confidence = detect_language(block.text) if confidence < 0.7: # Fallback to OCR for this block block = ocr_region(images, block.bbox) block.lang = lang # Stage 4: BiDi reordering if RTL if script_is_rtl(lang): block.text = bidi_reshape(block.text) # Stage 5: Normalization (NFKC for compatibility) return unicodedata.normalize('NFKC', ' '.join(block.text for block in ordered))