🖼️📄 Batch OCR & Text Compiler GUI 🚀

Transform folders full of images into organized text with a single click! This desktop application features a modern graphical interface to batch OCR (Optical Character Recognition) all images within a folder and then compile all extracted text into a single, chronologically sorted file. 🇪🇸🇬🇧 Supports OCR in Spanish and English.

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✨ Key Features

• Modern GUI: 💅 Simple, minimalist, and user-friendly interface built with CustomTkinter.
• Batch OCR: 🗂️ Processes all images (.png, .jpg, .jpeg, .bmp, .tiff) within the selected folder.
• Multilingual Support: 🇪🇸🇬🇧 Text recognition optimized for Spanish and English using EasyOCR.
• Chronological Order: 🕰️ Images are processed and text is compiled based on the image file's modification date and time (oldest first).
• Unique Output Filenames: 🛡️ Prevents overwriting of .txt files when images share the same base name but have different extensions (e.g., photo.png and photo.jpg), by saving as photo.png.txt and photo.jpg.txt.
• Text Compilation: 📝 Combines all extracted text from individual unique .txt files into a single output file.
• Clear Separators: 📑 The compiled file includes separators indicating the unique source .txt filename (e.g., imagename.ext.txt) for each text snippet.
• Overwrite Option: ❓ Prompts you whether to overwrite existing .txt files or use their content if OCR has already been performed.
• Background Processing: ⚙️ OCR and compilation run in a separate thread to keep the UI responsive.
• Detailed Status Logging: 📋 Displays progress and any errors in a textbox within the GUI.
• GPU Support: ⚡ Attempts to use GPU for faster OCR if available and configured (via PyTorch and CUDA).

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🛠️ Use Cases

• Document Digitization: 📄 Convert scans or photos of documents into editable text.
• Note Archiving: ✍️ Extract text from screenshots of presentations or handwritten notes (quality may vary).
• Data Collection: 📊 Gather textual information from large sets of images.
• Accessibility: 👁️‍🗨️ Create text versions of visual content for screen readers.
• Research Organization: 📚 Quickly group text from multiple visual sources into a single document for review.

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✅ Pros

• Easy to Use: 👌 The graphical interface simplifies the process; no command line needed for basic use.
• Time-Saving: ⏱️ Automates the tedious process of individually OCRing images.
• Organized: 🗄️ Chronological ordering and unique filenames/separators help maintain text traceability.
• Flexible: 🤸 The option to overwrite or use existing files is useful for resuming or updating.
• Potentially Portable: 💻 As a Python script, with the right dependencies, it could run on various operating systems.

⚠️ Cons

• Dependencies: 🧱 Requires installation of several Python libraries (CustomTkinter, EasyOCR, OpenCV, PyTorch, etc.).
• OCR Quality: 🖼️ OCR accuracy heavily depends on the quality of the input images (resolution, lighting, font clarity).
• Initial Model Download: ⏳ The first time it's used (or with a new language like Spanish), EasyOCR will download language models, which can take time.
• Resource Usage: 💻 OCR, especially in batch, can be CPU/GPU and memory intensive.
• Error Handling: 🐞 While errors are handled, very complex scenarios or heavily corrupted files might still cause issues.

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🚀 Getting Started

1. Clone the Repository (or download the .py file):

   git clone https://github.com/m4iccc/Python-OCR-Batch-script.git
   cd Python-OCR-Batch-script

   (Replace the URL and directory name accordingly)

2. Install Dependencies: Ensure you have Python 3.7+ installed. Then, install the required libraries using pip:

   pip install customtkinter easyocr opencv-python-headless numpy torch torchvision torchaudio

   Note: torch, torchvision, torchaudio are required by EasyOCR. Check the EasyOCR documentation for specific PyTorch version compatibility if you encounter issues, especially with GPU usage.

3. Run the Application: Navigate to the directory containing the script in your terminal and run:

   python Python OCR Batch script.py

   (Make sure to use the correct filename if you saved it differently).

4. Use the GUI:

   • Click the Browse... button to select the folder containing your images. The selected path will appear next to the button.
   • Click the large Click me to Batch OCR and Compile! button.
   • If existing unique .txt files (from previous runs, e.g., imagename.ext.txt) are found, you'll be prompted whether to Overwrite them (Yes), Use Existing ones where available (No), or Cancel the process.
   • Watch the progress and any messages in the status text box at the bottom.
   • Once completed, you'll find:
     • Individual unique .txt files for each processed image (e.g., photo.png.txt, scan.jpg.txt) inside your selected folder.
     • A single compiled file named _compiled_ocr_output_by_time.txt (or similar) inside the same folder, containing all the text ordered chronologically by image modification time.

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📝 License

This project is licensed under the GNU General Public License v3.0.

A copy of the license should be included in the repository (e.g., in a file named LICENSE). You can view the full license text here: https://www.gnu.org/licenses/gpl-3.0.html

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🙏 Acknowledgements

• CustomTkinter for the amazing modern GUI library.
• EasyOCR for the powerful and accessible OCR library.

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Feel free to fork, contribute, or open issues! ⭐