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TRK FTZ OCR

TRK FTZ OCR is a lightweight OCR toolkit for extracting text from PDFs and folders of PDFs.
It supports single-page extraction, full PDF processing, directory processing, parallel execution, and multiple output formats.


🚀 Features

  • 📄 Extract text from a single page
  • 📚 Process full PDF files
  • 📁 Process directories of PDFs
  • ⚡ Parallel processing for speed
  • 🧠 Adaptive OCR pipeline (PSM-based)
  • 📊 Export results to:
    • Dictionary (Python)
    • TXT files (per page / per file)
    • Excel (.xlsx)

📦 Installation

pip install trk-ftz-ocr

🖥️ CLI Usage

After installation, use:

trk-ftz-ocr --help

📄 Process a single PDF

Return text (default)

trk-ftz-ocr file.pdf --mode dict

Save as folder (per-page TXT files)

trk-ftz-ocr file.pdf --mode folder --output output/

Save as single Excel file

trk-ftz-ocr file.pdf --mode excel --output output.xlsx

📁 Process a directory

trk-ftz-ocr pdfs/ --mode folder --output output/

This creates:

output/
  file1/
    file1_page_1.txt
    file1_page_2.txt
  file2/
    file2_page_1.txt

⚙️ Options

Option Description
--page Extract a single page
--output Output path (file or folder)
--lang OCR language (default: ara)
--zoom Render quality (default: 6)
--parallel Enable parallel processing
--mode Output mode: dict, file, folder, excel

📊 Output Modes

dict

Returns Python dictionary:

{page_number: text}

file

Saves all text into a single .txt file.

folder

Saves each page as a separate .txt file.

excel

Saves results as:

file | page | text

🧠 Example (Python API)

from trk_ftz_ocr.pipeline import process

result = process(
    path="file.pdf",
    output_path="output/",
    mode="dict",
    parallel=True
)

print(result)

⚡ Performance

  • Parallel page processing (ThreadPool)
  • Parallel directory processing
  • Lightweight preprocessing pipeline
  • Adaptive OCR configuration

📌 Requirements

  • Python >= 3.12
  • Tesseract OCR installed on system

🛠️ Dependencies

  • PyMuPDF
  • pytesseract
  • pillow
  • tqdm
  • pandas
  • openpyxl

📄 License

MIT License

Metadata

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