Processing Khmer text in PDFs with Python is a specialized task due to the complex script, unique font rendering (like Khmer Unicode subscripts), and the lack of standard word spacing in the Khmer language. To achieve "verified" results—meaning text that is accurately rendered or extracted without breaking the script's visual logic—developers must use specific libraries and configurations. 1. Generating Verified Khmer PDFs with fpdf2
Creating a "verified" Khmer PDF requires a library that supports Complex Text Layout (CTL) and text shaping. Standard libraries often fail to render subscripts correctly, but the fpdf2 library has addressed these issues.
Key Requirement: Use Unicode fonts like "KhmerOS" or "KhmerMoul" to ensure official document standards are met.
Implementation: You must enable text shaping (pdf.set_text_shaping(True)) to correctly render Khmer subscripts and ligatures. 2. Extracting Khmer Text from PDFs
Extraction is significantly harder than generation because Khmer characters are often stored in non-standard encodings within PDF files.
Standard Extractors: Libraries like PyMuPDF (fitz) and pypdf are highly efficient for searchable PDFs.
The "Verified" OCR Route: If a PDF uses embedded fonts that don't map correctly to Unicode, the most "fool-proof" method is converting pages to images and using OCR (Optical Character Recognition).
Tesseract OCR: Supports Khmer (khm) and can be integrated via pytesseract or the Kreuzberg library for local processing.
EasyOCR: An alternative that supports over 80 languages and is optimized for deep learning performance. 3. Essential Python Libraries for Khmer Text
To verify and process the extracted text (e.g., word segmentation), use specialized Khmer NLP tools: Reddit·r/learnpythonhttps://www.reddit.com
Extracting text from a PDF without using PyPDF2 : r/learnpython
Here’s a short story inspired by the phrase “python khmer pdf verified” — blending coding, Cambodian heritage, and a quest for truth.
Title: The Verified Scroll
Sophea stared at the error message on her laptop:
"PDF structure invalid. Khmer Unicode missing glyphs."
It was 2 a.m. in Phnom Penh. Outside, the monsoon rain hammered corrugated roofs. Inside her tiny apartment, she was trying to digitize her grandfather’s memoir — a brittle, handwritten notebook from the Khmer Rouge era. But every scan-to-PDF conversion failed. The Khmer script turned into boxes and gibberish.
She wasn’t just a granddaughter. She was a data engineer.
“Python,” she whispered, opening a Jupyter notebook.
She wrote a script — khmer_pdf_verify.py — that did three things:
pypdf2 and pdfplumber.But the memoir kept failing verification at page 47.
Curious, Sophea printed that page. Under a dim lamp, she noticed something strange: the handwriting shifted midway down the page. Different ink. Different voice.
Her Python script hadn’t just been checking fonts — it had been verifying authenticity.
She added a new function: verify_consistency(text_chunks). It compared Khmer word n-grams across pages. Page 47’s later half showed a sudden drop in unique trigrams — a sign of copying or tampering.
A chill ran through her.
She called her mother in Battambang. “Mom, did grandfather ever mention someone else writing part of his diary?”
A long pause. Then: “He didn’t write all of it. A comrade finished the last chapters after… after the prison camp. The comrade survived. Grandfather didn’t.”
Sophea’s Python script had verified what family lore had long suspected: the memoir was genuine, but not single-authored. The Khmer script, broken in the PDF, held two souls.
She rebuilt the PDF — embedding subset fonts, fixing glyph mapping, adding a verification watermark:
"✅ python-khmer-pdf-verified :: dual provenance authenticated"
That night, she didn’t sleep. She uploaded the verified PDF to a public archive. Within a week, historians contacted her. Two years later, the memoir was cited in a landmark study on collaborative survival writing during the Khmer Rouge period.
All because a script refused to accept broken glyphs as the final word.
Sometimes, verification is not just about data integrity — it’s about honoring whose story is really being told.
Would you like the actual Python code for the khmer_pdf_verify.py script described in the story?
Since the phrase "verified — good content" suggests you want reliable sources, I have compiled a list of high-quality resources for learning Python in Khmer, including how to work with PDFs.
With the rise of AI and digital verification, we expect:
Until then, stick to the official sources listed above. Bookmark the Ministry of Education’s ICT page and join verified Telegram groups like Khmer Python Community (បញ្ជាក់ដោយ KPC).
Searching for "python khmer pdf verified" is not just about finding code—it's about finding trust. The Cambodian digital ecosystem deserves robust tools that respect the beauty and complexity of the Khmer script.
To recap the verified stack:
reportlab + embedded Khmer OS font.pdfminer.six with UTF-8.pypdf for merging/splitting.tesseract + language pack khm.Always verify your outputs with real users. Run your generated PDFs on Windows, macOS, and mobile PDF viewers. When the subscripts align and the vowels stay in place, your Python script is truly verified. python khmer pdf verified
Download the verified sample code and Khmer test PDFs from the Cambodia Python Developers GitHub repository (link in bio).
Have you encountered an unverified Khmer PDF library? Share your experience in the comments below.
To generate PDF content in Khmer using Python, you must handle Unicode (UTF-8) and TrueType font embedding, as standard PDF libraries often fail to render Khmer glyphs correctly without them. Recommended Tool: fpdf2
The fpdf2 library is a mature and actively maintained Python package that supports Khmer Unicode when paired with a compatible font like Battambang or Hanuman. Core Requirements
Font File: You must have a .ttf font file that supports Khmer Unicode. Library: Install via pip: pip install fpdf2. Example: Generating Khmer Content
The following script demonstrates how to embed a Khmer font and output a PDF with verified Khmer text rendering:
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
Font Embedding: Use pdf.add_font() to ensure the font is embedded in the PDF. Without this, the Khmer script may appear as boxes or garbled text on devices that don't have the specific font installed.
Encoding: Always ensure your Python script uses utf-8 encoding when reading external text files to maintain the integrity of Khmer characters.
Alternative for Extraction: If you need to extract verified Khmer text from an existing PDF, use libraries like multilingual-pdf2text, which uses Tesseract OCR for accurate recognition. Advanced: Writer Verification
If your "verified" requirement refers to forensic signature or handwriting verification, researchers have developed KhmerWriterID, a deep learning model (CNN/RNN) achieving over 99% accuracy in identifying specific Khmer handwriting styles.
For implementing verified Khmer language support in Python for PDF generation or text extraction, the primary solution involves using libraries that support Unicode UTF-8 text shaping (complex script rendering). 1. Generating Khmer PDFs with
library is the most straightforward, verified way to generate PDFs with Khmer script. It requires enabling text shaping to correctly render Khmer ligatures and subscripts. Step 1: Install the library pip install fpdf2 Use code with caution. Copied to clipboard Step 2: Use a Khmer Unicode Font You must provide a font file (e.g., KhmerOS.ttf Battambang-Regular.ttf ) as standard PDF fonts do not support Khmer. Step 3: Enable Text Shaping set_text_shaping(True) to ensure character clusters are rendered correctly. Example Implementation: = FPDF() pdf.add_page() # Path to your Khmer font file pdf.add_font( fonts/KhmerOS.ttf ) pdf.set_font( # Enable complex script rendering pdf.set_text_shaping( )
pdf.write( សួស្តី ពិភពលោក (Hello World) ) pdf.output( khmer_output.pdf Use code with caution. Copied to clipboard 2. Extracting Khmer Text from PDFs
Extracting Khmer is more difficult due to the complex nature of its script. There are two primary "verified" paths depending on the PDF type: Digitally Native PDFs (Text-based):
to extract metadata and text. However, if the PDF was created without proper Unicode mapping, the text might come out as garbled characters (mojibake). Scanned PDFs or Image-based Extraction (OCR): For "verified" accuracy, use Tesseract OCR with Khmer language data. multilingual-pdf2text pytesseract Requirements: You must have Tesseract installed on your system with the language pack. 3. Key Challenges and Solutions Ligatures and Subscripts:
Without text shaping, Khmer characters like subscripts (ជើង) will appear next to the main character instead of underneath it. Font Embedding: Always use subset embedding (supported by
) to ensure the PDF looks the same on all devices without requiring the recipient to have the font installed. Ensure your Python source file uses # -*- coding: UTF-8 -*- at the top and handle all strings as Unicode. Recommended Resources Official Documentation: fpdf2 Documentation specifically covers Unicode and complex scripts. Community Support: GitHub issues for py-pdf/fpdf2 contain verified code snippets for Khmer OS fonts. verified Khmer fonts that are known to work best with these Python libraries? multilingual-pdf2text - PyPI
To generate or verify Khmer Unicode in PDFs using Python, the most reliable and "verified" method involves using a library that supports complex text shaping
(handling ligatures and subscripts like the "Coeng" sign) and embedding high-quality Unicode fonts Battambang 1. Verified Python Library:
While many libraries struggle with Khmer's complex character clusters,
is widely recognized for its integrated support for HarfBuzz, a shaping engine that correctly reorders and positions Khmer glyphs. Implementation Checklist: Enable Shaping
: You must explicitly enable the shaping engine and specify the script/language codes ( Embed TTF Fonts
: Standard fonts like Helvetica or Arial cannot render Khmer; you must download and embed a TrueType font (.ttf). Absolute Paths
: Use absolute paths for font files to avoid "file not found" errors during verification. 2. Code Example for Verified Khmer PDF
The following script demonstrates how to correctly render Khmer script, ensuring subscripts (ligatures) appear correctly rather than as broken squares or misaligned marks. = FPDF() pdf.add_page()
# 1. Register a verified Khmer Unicode font (e.g., Battambang from Google Fonts) # Ensure the .ttf file is in your local directory pdf.add_font( Battambang-Regular.ttf ) pdf.set_font(
# 2. Enable the text shaping engine (Requires: pip install uharfbuzz)
# This is CRITICAL for correctly rendering "Coeng" (្) and clusters pdf.set_text_shaping(use_shaping_engine= , language= # 3. Add Khmer text pdf.write(
សួស្តី ពិភពលោក (Hello World in Khmer) ) pdf.write( ភ្នំពេញ (Phnom Penh - testing subscripts) )
pdf.output( khmer_verified.pdf Use code with caution. Copied to clipboard 3. Verified Verification & Extraction If you are trying to
existing Khmer content in a PDF (extraction), standard libraries like
often fail because they extract raw Unicode characters without understanding the layout. Issue on Khmer Unicode Font Subscripts #1187 - GitHub
Searching for "Python Khmer PDF" typically leads to resources for Natural Language Processing (NLP) or dataset processing specifically for the Khmer language. Verified Python Khmer PDF Resources Khmer Education PDF Dataset : A verified dataset on Hugging Face
containing cleaned text extracted from Khmer educational PDFs. It is recommended for: Educational content analysis. Khmer NLP research and development. Tokenization benchmarking. khmer Documentation Processing Khmer text in PDFs with Python is
: While named "khmer," this is a specialized Python library for genome sequence analysis (k-mer counting), not for the Khmer language. Documentation is available in PDF format Common Python Libraries for Khmer PDF Processing If you are looking to
content (extracting or creating PDFs) in Khmer using Python, you generally need tools that support Unicode and complex script rendering: Text Extraction PyMuPDF (fitz)
: Excellent for extracting text from PDFs while preserving Khmer Unicode characters. pdfplumber
: Good for extracting tables and structured text from Khmer documents. Creating PDFs : Requires a Khmer-compatible TrueType font (like Khmer OS Battambang
) to be registered within the script to render text correctly.
: A simpler library that also supports UTF-8 and external fonts for Khmer script. Python code snippet for extracting text from a Khmer PDF or for creating one?
Generating Khmer text in PDFs using Python requires specialized handling because Khmer is a complex script with intricate ligatures and character positioning (subscripts). Standard libraries often fail to render these correctly without text shaping engines.
The most effective, "verified" method for reliable Khmer PDF generation involves using modern libraries like fpdf2 paired with shaping tools. Recommended Libraries and Workflow 1. fpdf2 (with Text Shaping)
The fpdf2 library is currently the most accessible "verified" solution for Khmer. Unlike older versions, it supports a set_text_shaping method that correctly handles Khmer subscripts and vowel positioning when using the uharfbuzz engine. Key Requirements:
Font: You must use a TrueType Font (TTF) that supports Khmer, such as KhmerOS.ttf, KhmerMoul.ttf, or Battambang-Regular.ttf.
Text Shaping: Enable shaping to ensure characters don't appear as disconnected glyphs. 2. ReportLab (Advanced Design)
ReportLab is an industry-standard for complex layouts and charts. While powerful, it requires manual registration of UTF-8 fonts to display non-Latin characters.
Verification Note: ReportLab may require additional effort (like using external reshapers) to handle complex Khmer ligatures perfectly, as its native support for Indic scripts can be more complex to configure than fpdf2. Implementation Example (fpdf2) To produce a verified Khmer PDF, follow this structure:
from fpdf import FPDF pdf = FPDF() pdf.add_page() # 1. Register a Khmer-supporting font pdf.add_font("KhmerOS", fname="path/to/KhmerOS.ttf") pdf.set_font("KhmerOS", size=14) # 2. Enable the text shaping engine for Khmer (requires 'uharfbuzz' package) pdf.set_text_shaping(use_shaping_engine=True, script="khmr", language="khm") # 3. Write Khmer text pdf.write(8, "សួស្តី ពិភពលោក (Hello World)") pdf.output("khmer_document.pdf") Use code with caution. Copied to clipboard Critical Success Factors Developer FAQs - ReportLab Docs
Working with Khmer script in Python PDFs is famously tricky because Khmer uses complex text shaping (subscripts, clusters, and ligatures) that many standard libraries break.
To create a "verified" result—where the script looks exactly like it should—you need a tool that supports the HarfBuzz shaping engine. Recommended Tools
fpdf2: Currently the best choice for Python users. It has built-in support for Unicode and text shaping via uharfbuzz.
ReportLab: A professional-grade engine, though it requires more manual setup for complex shaping. Step-by-Step Guide: Creating Khmer PDFs with fpdf2 1. Install Requirements
You need fpdf2 and uharfbuzz (which handles the complex layout logic). pip install fpdf2 uharfbuzz Use code with caution. Copied to clipboard 2. Get a Compatible Khmer Font
Standard fonts like Helvetica won't work. Download a Khmer TrueType Font (.ttf), such as Battambang or Kantumruy from Google Fonts. 3. Python Implementation
This script uses the shaping engine to ensure subscripts and vowels are positioned correctly.
from fpdf import FPDF # 1. Initialize PDF pdf = FPDF() pdf.add_page() # 2. Register your Khmer font (crucial: use a .ttf file) # Replace 'fonts/Battambang-Regular.ttf' with your actual path pdf.add_font("KhmerFont", style="", fname="Battambang-Regular.ttf") pdf.set_font("KhmerFont", size=16) # 3. Enable the shaping engine for Khmer clusters # This ensures characters like '្' or 'ុ' render correctly pdf.set_text_shaping(True) # 4. Write Khmer text khmer_text = "សួស្តីពិភពលោក (Hello World)" pdf.cell(w=0, h=10, text=khmer_text, align='C', new_x="LMARGIN", new_y="NEXT") # 5. Output PDF pdf.output("khmer_verified.pdf") Use code with caution. Copied to clipboard Common Issues & Fixes
Broken Characters (Boxes): Ensure you are using pdf.add_font() with a font that actually contains Khmer glyphs. Built-in fonts like Arial or Times-Roman do not support Khmer.
Incorrect Layout (Subscripts flying away): If fpdf2 is not shaping correctly, verify that uharfbuzz is installed and that you've explicitly called pdf.set_text_shaping(True).
Mixed English/Khmer Alignment: When mixing scripts, sometimes the "guess" for script direction fails. You can manually set the script by passing script="Khmr" to the text methods if needed. Chapter 3: Fonts - ReportLab Docs
Mastering Python Khmer PDF Processing: A Verified Guide Working with Khmer Unicode in PDFs using Python requires specialized handling due to the script's complex rendering requirements, such as consonant subscripts and vowel positioning. This guide provides verified methods for generating and extracting Khmer text in PDF format. 1. Generating Khmer PDFs with Python
To correctly render Khmer script, you must use a library that supports text shaping (integrating characters into correct glyph sequences) and embed a compatible Khmer font. Using fpdf2 (Recommended)
fpdf2 is a modern library that supports HarfBuzz-based text shaping, essential for Khmer script. Verified Setup: Install the library: pip install fpdf2.
Download a Unicode Khmer font like Battambang, KhmerOS, or Noto Sans Khmer. Enable text shaping in your code:
from fpdf import FPDF pdf = FPDF() pdf.add_page() # Register and set the Khmer font pdf.add_font("KhmerOS", fname="KhmerOS.ttf") pdf.set_font("KhmerOS", size=14) # CRITICAL: Enable text shaping for correct rendering pdf.set_text_shaping(True) pdf.write(8, "សួស្តី ពិភពលោក (Hello World)") pdf.output("khmer_verified.pdf") ``` Use code with caution. Using ReportLab
ReportLab is powerful for complex layouts but requires manual font registration for Khmer.
Step: Use pdfmetrics.registerFont to load your .ttf file before drawing strings.
Limitation: Older versions may struggle with advanced Khmer shaping without additional plugins like uharfbuzz. 2. Extracting Khmer Text from PDFs
Extracting text from Khmer PDFs is often difficult because many extractors fail to reconstruct the complex character clusters.
Based on your request for a verified paper covering Python, Khmer, and PDF, the most relevant authoritative research involves Writer Verification and Text Recognition for the Khmer language using deep learning frameworks often implemented in Python. Featured Verified Paper
The primary academic work addressing these specific topics is:
KhmerWriterID: Toward Robust Khmer Writer Verification Using Deep Learning (March 2026). Title: The Verified Scroll Sophea stared at the
Focus: This paper addresses word-level Khmer writer verification—determining if two samples were written by the same person.
Technology: It uses a hybrid Siamese architecture (CNN + RNN), which is commonly developed and tested in Python environments.
PDF Source: A full-text version is hosted by the UHST Library. Related Verified Research (Python & Khmer)
If you are looking for specific technical implementations, consider these papers:
Khmer Text Extraction and Corpus Construction:A 2026 paper titled Large Language Model-Based Multi-Agent System for Automated Khmer Text Extraction explores using AI agents to extract Khmer text from complex documents.
Khmer Optical Character Recognition (OCR):Enhancing Khmer Optical Character Recognition By Using Fine-Tuning Tesseract (Sept 2025) provides a methodology for improving OCR accuracy for official Khmer documents. This type of research frequently uses Python-based libraries like pytesseract.
Khmer Textline Recognition:Hybrid Convolutional Khmer Textline Recognition Method (July 2024) introduces a Transformer-based network for recognizing long Khmer textlines, a task essential for digitizing Khmer PDFs. Important Distinction: "khmer" Python Library
It is important to note that a popular Python software package named khmer exists, but it is unrelated to the Cambodian language.
The khmer software package: This is a widely cited tool for DNA sequence analysis and bioinformatics. If your interest is in bioinformatics, this verified documentation is available as a PDF.
I understand you're looking for a detailed article related to Python and Khmer (Cambodian) language processing, specifically for verified PDF content.
However, I cannot directly generate or provide a verified PDF file. What I can do is provide you with a detailed, ready-to-publish article that you can save as a PDF yourself. Below is a comprehensive guide on using Python to process Khmer text, extract data from PDFs, and validate results — written for developers and researchers working with the Khmer script.
If you are a developer trying to verify, process, or create PDFs containing Khmer text using Python, here are the standard libraries used:
A. Creating Khmer PDFs (ReportLab)
Standard PDF libraries sometimes fail to render Khmer script correctly because of complex ligatures. The reportlab library is commonly used, but you must register a Khmer-compatible font (like Khmer OS Battambang or Khmer OS Siemreap).
from reportlab.pdfgen import canvas
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
# Register a Khmer Font (ensure you have the .ttf file)
pdfmetrics.registerFont(TTFont('KhmerOS', 'KhmerOS.ttf'))
c = canvas.Canvas("khmer_document.pdf")
c.setFont("KhmerOS", 12)
c.drawString(100, 750, "សួស្តីពិភពលោក") # Hello World in Khmer
c.save()
B. Extracting Text from Khmer PDFs (PyMuPDF / pdfplumber) To read or verify text inside a Khmer PDF:
C. Khmer Word Segmentation (KhmerNLP)
Unlike English, Khmer does not use spaces between words. If you are verifying text, you might need to segment the words first. The khmernlp library is useful here:
# pip install khmernlp
from khmernlp import word_tokenize
sentence = "ខ្ញុំចូលចិត្តសិក្សាភាសាខ្មែរ"
words = word_tokenize(sentence)
print(words)
# Output: ['ខ្ញុំ', 'ចូលចិត្ត', 'សិក្សា', 'ភាសាខ្មែរ']
If you have a specific file you would like me to analyze or summarize, please provide the text or upload the file if your interface allows it.
Working with Khmer script in PDF files using Python presents unique challenges due to complex Unicode shaping and font rendering. Whether you are building an automated verification system or an OCR pipeline, 1. The Core Challenge: Khmer Script in PDFs
Khmer is a "complex" script. Unlike Latin characters, Khmer involves vowel signs and subscripts that must be "shaped" (repositioned and reordered) by a rendering engine to look correct. Standard PDF libraries often fail to read or write these characters properly because they treat them as individual, static glyphs rather than a cohesive linguistic unit. 2. Best Tools for Extracting Khmer Text
To verify the content of a Khmer PDF, you first need to reliably extract it. Depending on whether the PDF is "searchable" (digital) or "scanned" (images), you have two main paths: For Searchable Digital PDFs
fpdf2: This library is highly recommended for Khmer because it supports a shaping engine (Harfbuzz). To ensure subscripts and vowels are handled correctly, you must explicitly set the script and language:
pdf.set_text_shaping(use_shaping_engine=True, script="khmr", language="khm") ``` Use code with caution. Copied to clipboard
PyMuPDF: Widely considered one of the fastest and most accurate open-source libraries for text extraction. It preserves document structure better than many alternatives. For Scanned PDFs (OCR)
If the PDF contains images of text, you must use Optical Character Recognition (OCR):
Tesseract OCR: You can install the Khmer-specific language pack (tesseract-ocr-khm) and use the pytesseract wrapper to extract text.
NextOCR: A specialized tool by Khmer font expert Danh Hong that offers high-accuracy extraction in just a few lines of code. 3. Verifying Document Integrity
"Verification" typically refers to two things: ensuring the file is a valid PDF and checking digital signatures. Checking File Validity
Before processing, verify that the file is not corrupted or merely a renamed extension. You can use the file command via subprocess to check the MIME type:
from subprocess import Popen, PIPE filetype = Popen("/usr/bin/file -b --mime -", shell=True, stdout=PIPE, stdin=PIPE).communicate(open("file.pdf", "rb").read(1024))[0] ``` #### Verifying Digital Signatures To verify that a signed Khmer document hasn't been altered: * **[pyHanko](https://pyhanko.readthedocs.io/en/latest/cli-guide/validation.html)**: A robust library for validating PDF signatures. It can provide a "pretty-print" status report of a signature's validity. * **[pypdf](https://github.com/py-pdf/pypdf/discussions/2678)**: Useful for quickly detecting if a PDF has been digitally signed at all by checking the `/Root` and `/AcroForm` flags. ### 4. Advanced NLP Verification If your goal is to verify the *linguistic* correctness of extracted Khmer text (e.g., checking for typos or proper word breaks), you should integrate: * **[khmer-nltk](https://medium.com/data-science/khmer-natural-language-processing-in-python-c770afb84784)**: Excellent for word segmentation and part-of-speech tagging. * **[PyKhmerNLP](https://pypi.org/project/pykhmernlp/)**: Provides modules for dictionary lookups and address processing to help validate the actual data you've extracted. Would you like a **specific code example** for extracting Khmer text from a scanned PDF using Tesseract? Use code with caution. Copied to clipboard
This is an excellent topic, as it sits at the intersection of Southeast Asian NLP (low-resource languages), digital document forensics, and Python automation.
Below is a structured, ready-to-use template for a research paper or technical report. You can fill in the specific data based on your implementation.
khmer_content = extract_khmer_from_pdf('khmer_document.pdf') print(khmer_content[:500]) # First 500 chars
pip install pypdf2 pdfplumber pytesseract pillow pandas khmer-nltk
For OCR support on scanned PDFs:
sudo apt-get install tesseract-ocr-khm # Linux
# or download Khmer trained data for Windows/macOS
[1] Chea, S., & Bird, S. (2019). "Challenges in Khmer NLP: Subscript ordering and Unicode normalization." Journal of Southeast Asian Linguistics.
[2] Adobe Systems. (2020). PDF Reference 2.0 – Chapter 9: Text Extraction.
[3] Python Software Foundation. pypdf library documentation.
[4] National Institute of Standards (NIST). "SHA-3 Standard: Permutation-Based Hash Functions." FIPS 202.