'link': Python Khmer Pdf Verified
# Normalization: Khmer requires NFC form normalized = unicodedata.normalize('NFC', text)
If you'd like to dive deeper into a specific stage of this process, let me know:
Standard PDF libraries often fail to correctly parse or generate Khmer text, resulting in broken characters, missing sub-consonants, or incorrect word ordering. This guide provides verified, actionable workflows to successfully extract and generate Khmer PDFs using Python. Part 1: Verified Khmer PDF Generation
Khmer is a complex script where characters reorder or stack (subscripts). Standard PDF libraries like the original python khmer pdf verified
Standard ReportLab cannot handle Khmer text shaping out of the box. To generate PDFs correctly, you must pair it with a text-shaping wrapper or use a library like weasyprint which relies on a browser-grade rendering engine (Pango).
Khmer features subscript consonants (Cheung akhar) and vowels that stack vertically or wrap around base characters. Standard PDF engines often break these clusters.
If the font encoding in the PDF is corrupted, or if the PDF consists of scanned images, you must use Tesseract OCR configured with the Khmer language pack ( khm ). 1. Install System Requirements Install Tesseract OCR on your machine. # Normalization: Khmer requires NFC form normalized =
def extract_with_fallback(pdf_path): reader = PdfReader(pdf_path) full_text = "" for page in reader.pages: text = page.extract_text() # Check for mojibake (e.g., ➊ instead of ខ) if 'â' in text or '\ufffd' in text: # Attempt recoding: this is heuristic text = text.encode('latin1').decode('utf-8', errors='ignore') full_text += text return full_text
# 3. CRITICAL: Enable text shaping for correct Khmer subscripts pdf.set_text_shaping( # 4. Write Khmer text khmer_text សួស្តីពិភពលោក (Hello World) , khmer_text)
def segment(self): return segment_khmer_words(self.verified_text) Standard PDF libraries like the original Standard ReportLab
Therefore, any Python solution for Khmer PDF verification must first overcome this foundational challenge of correctly handling the script.
from fpdf import FPDF # 1. Initialize PDF pdf = FPDF() pdf.add_page() # 2. Add and Set Khmer Font # Ensure 'Battambang-Regular.ttf' is in your script directory pdf.add_font('Battambang', fname='Battambang-Regular.ttf') pdf.set_font('Battambang', size=16) # 3. Add Khmer Content khmer_text = "សួស្តីពិភពលោក (Hello World in Khmer)" pdf.cell(w=0, h=10, text=khmer_text, new_x="LMARGIN", new_y="NEXT", align='C') # 4. Save the PDF pdf.output("khmer_verified_output.pdf") Use code with caution. Copied to clipboard Key Considerations for Verification
with pdfplumber.open("khmer_document.pdf") as pdf: for page in pdf.pages: khmer_text = page.extract_text() if khmer_text: print("Extracted Khmer Text:") print(khmer_text)
def verify_checksum(file_path, expected_md5): md5_hash = hashlib.md5() with open(file_path, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): md5_hash.update(chunk) return md5_hash.hexdigest() == expected_md5
Python offers several libraries for working with PDFs, including PyPDF2, pdfminer, and ReportLab. These libraries provide functionalities for reading, writing, and manipulating PDFs. However, working with PDFs in Khmer requires additional considerations due to the language's unique script and encoding.
