#!/usr/bin/env python3 """PDF text extraction helpers mirroring ipfs_datasets_py IntegratedPDFProcessor. Prefer pdfplumber -> pymupdf -> pdftotext; OCR (tesseract) only when text layer is missing or garbage. Honest article splits require >=2 heading matches. """ from __future__ import annotations import io import logging import re import subprocess import tempfile from typing import Optional log = logging.getLogger("pdf_extract_lib") # Spaced-out OCR garbage like "D IA R IO" _GARBAGE_SPACING = re.compile(r"(?:^[A-Za-z]\s+){6,}", re.M) def is_garbage_text(text: str) -> bool: if not text or len(text) < 40: return True if text.lstrip().startswith("%PDF") or "endobj" in text[:200]: return True if "IHDR" in text[:120] or text[:8].startswith("\x89PNG") or "PNG" in text[:20] and "IHDR" in text[:80]: return True sample = text[:8000] # Broken ToUnicode / CID font dumps from Arabic/Persian PDFs if sample.count("(cid:") >= 8: return True arabic = sum(1 for ch in sample if "\u0600" <= ch <= "\u06FF") ctrl = sum(1 for ch in sample if ord(ch) < 32 and ch not in "\n\r\t\x0c") n = max(1, len(sample)) # Encoding-corrupt Arabic/Persian PDFs: many controls / almost no Arabic script if len(sample) > 500 and (ctrl / n) > 0.04 and (arabic / n) < 0.05: return True if len(sample) > 800 and arabic < 80 and sample.count("@") > 30: return True letters = sum(ch.isalpha() for ch in sample) spaces = sample.count(" ") if letters < 80 and arabic < 40: return True # Mojibake Latin stand-in for Arabic (common in old MoJ PDFs) ascii_letters = sum(ch.isalpha() and ord(ch) < 128 for ch in sample) if len(sample) > 500 and arabic < 20 and ascii_letters > 400: return True if spaces > letters * 1.8 and _GARBAGE_SPACING.search(sample): return True singles = len(re.findall(r"(?m)^\s*[A-Za-zÁÉÍÓÚÑáéíóúñ]\s*$", sample)) if singles > 30 and singles > letters * 0.15: return True return False def extract_pdfplumber(raw: bytes, max_pages: int = 200) -> tuple[str, int]: import pdfplumber with pdfplumber.open(io.BytesIO(raw)) as pdf: n = len(pdf.pages) parts = [] for page in pdf.pages[:max_pages]: try: parts.append(page.extract_text() or "") except Exception: continue text = "\n".join(parts) text = re.sub(r"[ \t]+", " ", text) text = re.sub(r"\n{3,}", "\n\n", text).strip() return text, n def extract_pymupdf(raw: bytes, max_pages: int = 200) -> tuple[str, int]: import pymupdf doc = pymupdf.open(stream=raw, filetype="pdf") n = doc.page_count parts = [page.get_text() for page in doc[:max_pages]] text = "\n".join(parts) text = re.sub(r"[ \t]+", " ", text) text = re.sub(r"\n{3,}", "\n\n", text).strip() return text, n def extract_pdftotext(raw: bytes) -> str: if not raw or raw[:4] != b"%PDF": return "" try: with tempfile.NamedTemporaryFile(suffix=".pdf", delete=True) as tmp: tmp.write(raw) tmp.flush() proc = subprocess.run( ["pdftotext", "-layout", "-enc", "UTF-8", tmp.name, "-"], check=False, capture_output=True, timeout=180, ) if proc.returncode == 0 and proc.stdout: return proc.stdout.decode("utf-8", "replace").strip() except Exception as exc: log.warning("pdftotext: %s", exc) return "" def extract_ocr(raw: bytes, lang: str = "eng", max_pages: int = 40, dpi: int = 120) -> tuple[str, int]: """Render pages with pymupdf and OCR via tesseract CLI.""" import pymupdf doc = pymupdf.open(stream=raw, filetype="pdf") n = doc.page_count parts = [] mat = pymupdf.Matrix(dpi / 72, dpi / 72) for i, page in enumerate(doc): if i >= max_pages: break pix = page.get_pixmap(matrix=mat, alpha=False) with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as img: pix.save(img.name) img_path = img.name try: env = dict(**__import__("os").environ) env["OMP_THREAD_LIMIT"] = "1" env["TESSDATA_PREFIX"] = env.get("TESSDATA_PREFIX", "/usr/share/tesseract-ocr/5/tessdata") proc = subprocess.run( ["tesseract", img_path, "stdout", "-l", lang, "--psm", "6"], capture_output=True, text=True, timeout=180, env=env, ) if proc.returncode == 0 and proc.stdout: parts.append(proc.stdout) except Exception as exc: log.debug("ocr page %s: %s", i, exc) finally: try: import os os.unlink(img_path) except Exception: pass text = "\n".join(parts) text = re.sub(r"[ \t]+", " ", text) text = re.sub(r"\n{3,}", "\n\n", text).strip() return text, n def extract_pdf_text( raw: bytes, *, enable_ocr: bool = True, ocr_lang: str = "eng", ocr_max_pages: int = 40, ) -> tuple[str, str, int]: """Return (text, method, page_count). Mirrors IntegratedPDFProcessor order.""" if not raw: return "", "not_pdf", 0 if raw[:4] != b"%PDF": idx = raw.find(b"%PDF", 0, 65536) if idx < 0: return "", "not_pdf", 0 raw = raw[idx:] best_text, best_method, pages = "", "failed", 0 for name, fn in ( ("pdfplumber", lambda: extract_pdfplumber(raw)), ("pymupdf", lambda: extract_pymupdf(raw)), ): try: text, pages = fn() if text and not is_garbage_text(text) and len(text) >= 80: return text, name, pages if len(text) > len(best_text): best_text, best_method = text, name except Exception as exc: log.debug("%s failed: %s", name, exc) try: text = extract_pdftotext(raw) if text and not is_garbage_text(text) and len(text) >= 80: return text, "pdftotext", pages if len(text) > len(best_text): best_text, best_method = text, "pdftotext" except Exception: pass if enable_ocr and (is_garbage_text(best_text) or len(best_text) < 120): try: text, pages = extract_ocr(raw, lang=ocr_lang, max_pages=ocr_max_pages) if text and len(text) >= 80 and not is_garbage_text(text): return text, "ocr_tesseract", pages if len(text) > len(best_text): best_text, best_method = text, "ocr_tesseract" except Exception as exc: log.debug("ocr failed: %s", exc) return best_text, best_method if best_text else "failed", pages def normalize_bidi(text: str) -> str: return re.sub(r"[\u200e\u200f\u202a-\u202e\u2066-\u2069\u200c\u200d\ufeff]", "", text or "") _EASTERN_DIGITS = str.maketrans( "\u0660\u0661\u0662\u0663\u0664\u0665\u0666\u0667\u0668\u0669" "\u06f0\u06f1\u06f2\u06f3\u06f4\u06f5\u06f6\u06f7\u06f8\u06f9", "01234567890123456789", ) def normalize_rtl_text(text: str) -> str: """Strip bidi marks and map Arabic/Persian digits to ASCII for heading regexes.""" t = normalize_bidi(text or "") t = re.sub(r"[\u00ad\u200b]", "", t) return t.translate(_EASTERN_DIGITS) def split_by_pattern( text: str, law_id: str, source_url: str, pat: re.Pattern, record_type: str = "article", min_chunk: int = 40, max_docs: int = 4000, ) -> list[dict]: matches = list(pat.finditer(text or "")) if len(matches) < 2: return [] docs = [] for i, m in enumerate(matches): start = m.start() end = matches[i + 1].start() if i + 1 < len(matches) else len(text) chunk = text[start:end].strip() if len(chunk) < min_chunk: continue num = re.sub(r"\s+", " ", m.group(1)).strip() aid = re.sub(r"[^\w\u0600-\u06FF\u1200-\u137F]+", "-", num.lower(), flags=re.U).strip("-")[:80] heading = chunk.split("\n", 1)[0][:200] docs.append({ "id": f"{law_id}-{aid}"[:180], "title": heading, "text": chunk, "date_filed": None, "document_number": num, "source_url": source_url, "record_type": record_type, "article_number": num, "law_identifier": law_id, "metadata": { "text_extraction": { "source": "official", "backend": "honest_heading_split", "parser": "ipfs_datasets_py.IntegratedPDFProcessor+heading_split", "pattern": pat.pattern[:100], } }, }) if len(docs) >= max_docs: break return docs if len(docs) >= 2 else [] # Country-specific honest patterns (tried in order) COUNTRY_PATTERNS: dict[str, list[tuple[str, re.Pattern, str]]] = { "sv": [ ("articulo", re.compile(r"(?im)^\s*((?:Art(?:[íi]culo|\.)?|ART[IÍ]CULO)\s+\d+[º°a-z.]*)"), "article"), ], "tn": [ ("fasl", re.compile(r"(?m)^\s*((?:الفصل|المادة)\s+\S+)"), "article"), ("article", re.compile(r"(?im)^\s*((?:Article|Art\.)\s+\d+)"), "article"), ], "et": [ ("article_heading", re.compile(r"(?im)^\s*((?:Article|Art\.)\s+\d+\.?)\s*(?:[.\-–—:]|\s+[A-Z\"“])"), "article"), ("article_loose", re.compile(r"(?im)^\s*((?:Article|Art\.)\s+\d+)"), "article"), ("article_inline", re.compile(r"(?im)((?:Article|Art\.)\s+\d+)(?![\d])"), "article"), ("anqets", re.compile(r"(?m)^\s*((?:አንቀጽ|አንቀፅ)\s*[/\.]?\s*\d+)"), "article"), ("anqets_inline", re.compile(r"(?m)((?:አንቀጽ|አንቀፅ)\s*[/.]?\s*\d+)"), "article"), ], "jm": [ ("section", re.compile(r"(?im)^\s*((?:Section|SECTION)\s+\d+[A-Za-z]?\.?)"), "section"), ("part", re.compile(r"(?im)^\s*((?:PART|Part)\s+[IVXLC\d]+)"), "section"), ], "fj": [ ("numbered_emdash", re.compile(r"(?m)^\s*((?:\d+)\.—)"), "article"), ("section", re.compile(r"(?im)^\s*((?:Section|SECTION|Article|ARTICLE)\s+\d+)"), "article"), ("chapter", re.compile(r"(?im)^\s*((?:CHAPTER|Chapter)\s+\d+)"), "section"), ], "la": [ ("article", re.compile(r"(?im)^\s*((?:Article|Art\.|ມາດຕາ)\s*\d+)"), "article"), ], "lk": [ ("section", re.compile(r"(?im)^\s*((?:Section|SECTION)\s+\d+[A-Za-z]?)"), "section"), ("article", re.compile(r"(?im)^\s*((?:Article|ARTICLE)\s+\d+)"), "article"), ], "md": [ ("articolul", re.compile(r"(?im)^\s*((?:Articolul|Art\.)\s*\d+(?:\^[0-9]+)?)"), "article"), ("articolul_inline", re.compile(r"(?im)((?:Articolul|Art\.)\s*\d+(?:\^[0-9]+)?)\s*[.\-–—:]"), "article"), ], "iq": [ ("mada", re.compile(r"(?m)^\s*((?:المادة|مادة)\s*[-–—:]*\s*\d+)(?!\d)"), "article"), ("mada_ocr", re.compile(r"(?m)^\s*((?:المادة|مادة)\s*[-–—:\s]*\d+)(?!\d)"), "article"), ("fasl", re.compile(r"(?m)^\s*((?:الفصل|الباب)\s+\S+)"), "section"), ("article_en", re.compile(r"(?im)^\s*((?:Article|Art\.?)\s+\d+)(?!\d)"), "article"), ], "ir": [ ("mada", re.compile(r"(?m)^\s*((?:ماده)\s*(?:واحده|\d+))(?!\d)"), "article"), ("mada_prefixed", re.compile( r"(?m)^\s*(?:\d+[\-–—ـ.)\]]*\s*)?((?:ماده)\s*(?:واحده|\d+))(?!\d)" ), "article"), ("fasl_bab", re.compile( r"(?m)^\s*((?:فصل|باب)\s*(?:اول|دوم|سوم|چهارم|پنجم|ششم|هفتم|هشتم|نهم|دهم|\d+))" ), "section"), ("article_en", re.compile(r"(?im)^\s*((?:Article|Art\.?)\s+\d+)(?!\d)"), "article"), ], "mz": [ ("artigo", re.compile(r"(?im)^\s*((?:Artigo|Art\.|ARTIGO)\s+\d+[ºªo°a-zA-Z]?)"), "article"), ], } def honest_split(cc: str, text: str, law_id: str, source_url: str) -> tuple[list[dict], str]: if cc in ("tn", "iq", "ir", "dz", "lb", "ma", "eg", "sa", "ae"): text_n = normalize_rtl_text(text) else: text_n = text for name, pat, rtype in COUNTRY_PATTERNS.get(cc, []): docs = split_by_pattern(text_n, law_id, source_url, pat, rtype) if docs: return docs, name # shared fallback from common import split_articles docs = split_articles(text_n, law_id, source_url) if len(docs) >= 2: return docs, "common_article_split" return [], "none"