📄 Image2FILE

A GTK4 application that converts document images to organized PDFs using OCR technology. It automatically detects page numbers, organizes documents, and allows manual corrections when OCR fails.

Features

• Parallel OCR Processing: Uses multiple CPU cores for faster image processing
• Automatic Page Detection: Extracts page numbers using Tesseract OCR
• Manual Correction: Interactive dialog for correcting OCR failures
• Smart Organization: Automatically organizes PDFs by page numbers
• Cache System: Skips already processed images to avoid reprocessing
• Modern UI: Built with GTK4 and Libadwaita for a native Linux experience
• Real-time Logs: Live monitoring of processing status and errors
• Configurable Settings: Adjustable maximum pages and processing threads

Prerequisites

System Requirements

• Linux operating system
• Python 3.8 or higher
• GTK4 development libraries
• Tesseract OCR engine

Installing System Dependencies

Ubuntu/Debian

sudo apt update
sudo apt install python3 python3-pip tesseract-ocr tesseract-ocr-por libgtk-4-dev libadwaita-1-dev

Fedora

sudo dnf install python3 python3-pip tesseract tesseract-langpack-por gtk4-devel libadwaita-devel

Arch Linux

sudo pacman -S python python-pip tesseract tesseract-data-por gtk4 libadwaita

Installation

1. Clone the repository:

git clone https://github.com/EmanuProds/ncx-book-organizer.git
cd img2doc

2. Create a virtual environment (recommended):

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Python dependencies:

pip install pytesseract pillow pygobject

Usage

1. Activate the virtual environment (if created):

source venv/bin/activate

2. Run the application:

python main.py

How to Use

1. Select Input Directory: Choose the folder containing your document images (JPG/JPEG)
2. Select Output Directory: Choose where the organized PDFs will be saved
3. Configure Settings (optional):
   • Maximum pages: Set the total number of pages in your document
   • Number of processes: Adjust parallel processing (0 = auto-detect)
4. Start Processing: Click "Start Processing" and monitor progress in the Logs tab
5. Manual Corrections: If OCR fails, the app will prompt for manual page number input

Output Structure

The application creates organized PDFs with the following naming convention:

• FL. 001.pdf, FL. 002.pdf, etc. - Regular pages
• FL. 001-verso.pdf - Back sides of pages
• TERMO DE ABERTURA.pdf - Opening terms
• TERMO DE ENCERRAMENTO.pdf - Closing terms
• ERRO_OCR_filename.pdf - Files that couldn't be processed

Configuration

OCR Settings

• Language: Portuguese (por)
• PSM Mode: 6 (Uniform block of text)
• ROI: Configurable region of interest for page number detection

Processing Settings

• Maximum Pages: Default 300 pages
• Parallel Processes: Default 4 workers
• Cache System: Automatically detects and skips already processed files

Architecture

The application follows a modern, service-oriented architecture with clear separation of concerns:

src/
├── models.py           # Data models and domain entities (dataclasses & enums)
├── exceptions.py       # Custom exception hierarchy
├── config.py           # Application configuration
├── core.py             # Legacy processing logic (backward compatibility)
├── services/           # Modern service layer
│   ├── file_service.py     # File operations and caching
│   ├── ocr_service.py      # OCR processing and image manipulation
│   └── processing_service.py # Main processing coordination
├── interface/          # GTK4 UI layer
│   ├── entrypoint.py       # Application initialization
│   ├── gui.py              # Main window and navigation
│   ├── home.py             # Processing interface
│   ├── pref.py             # Preferences/settings page
│   ├── logs.py             # Logging interface
│   └── about.py            # About dialog
├── ocr.py              # Legacy OCR functions (deprecated)
└── __init__.py         # Package initialization

Performance Tips

• Use SSD storage for faster I/O
• Increase parallel processes for multi-core systems
• Process images in batches for better cache utilization

License

This project is licensed under the MIT License.