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https://github.com/carlosacchi/captiocr

CaptiOCR - A real-time screen text extraction tool using Tesseract OCR. Capture, recognize, and log on-screen text dynamically. Future updates will include on-demand language installation, resizable selection areas, and live text overlays.
https://github.com/carlosacchi/captiocr

captions live live-caption live-captioning live-captions live-transcript logging ocr ocr-python ocr-recognition saver transcription

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CaptiOCR - A real-time screen text extraction tool using Tesseract OCR. Capture, recognize, and log on-screen text dynamically. Future updates will include on-demand language installation, resizable selection areas, and live text overlays.

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# 🖥️ CaptiOCR - Real-Time Screen Text Extraction



[![GitHub Release](https://img.shields.io/github/v/release/CarloSacchi/CaptiOCR)](https://github.com/CarloSacchi/CaptiOCR/releases/latest)

[![CodeQL](https://github.com/carlosacchi/captiocr/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/carlosacchi/captiocr/actions/workflows/github-code-scanning/codeql)



**CaptiOCR** is an open-source **real-time screen text extraction tool** designed to capture and transcribe captions (subtitles) from video conferencing applications like **Microsoft Teams**, **Zoom**, and **Google Meet**. With an intuitive interface and powerful OCR capabilities, you can select any screen area and extract text continuously in real-time.



---



## ✨ Key Features



✅ **Real-time OCR processing** using [Tesseract OCR](https://github.com/tesseract-ocr/tesseract)  

✅ **Multi-language support** (English, Italian, French, German, Portuguese)  

✅ **Multi-monitor support** with DPI awareness  

✅ **Dynamic area selection** - drag, resize, and move capture areas during operation  

✅ **Text processing** - automatic duplicate removal and text cleaning  

✅ **Profile management** - save and load different configurations  

✅ **Hotkey support** - `Ctrl+Q` to stop capture  

✅ **Export options** - save captured text with custom naming  

✅ **Debug logging** for troubleshooting  

✅ **Modular architecture** - clean, maintainable codebase  



---



## 🛠️ Prerequisites



Before installation, ensure you have:

- ✅ **Python 3.9+** installed  

- ✅ **Tesseract OCR** installed ([Download here](https://github.com/tesseract-ocr/tesseract))  

- ✅ **Windows OS** (primary support)  



---



## 📦 Installation



### **1️⃣ Clone the Repository**

```bash

git clone https://github.com/CarloSacchi/CaptiOCR.git

cd CaptiOCR

```



### **2️⃣ Install Python Dependencies**

```bash

pip install -r requirements.txt

```



### **3️⃣ Install Tesseract OCR**



**Windows users:**  

Download and install Tesseract from the [official releases](https://github.com/tesseract-ocr/tesseract/releases).  

The application will automatically detect standard installation paths.



---



## 🚀 Quick Start



Run the application:

```bash

python CaptiOCR.py

```



### **Basic Usage:**



1️⃣ **Select Language** - Choose your OCR language from the dropdown  

2️⃣ **Click "Start (Select Area)"** - Open the area selection tool  

3️⃣ **Drag to Select** - Draw a rectangle around the text area you want to capture  

4️⃣ **Press ENTER** - Begin real-time text extraction  

5️⃣ **Press Ctrl+Q or STOP** - End the capture session  

6️⃣ **Name Your Capture** - Save with a custom filename  



📁 **Output:** Captured text is saved in the `captures/` folder as timestamped `.txt` files.



---



## 🎯 Advanced Features



### **Multi-Monitor Support**

- **Automatic detection** of all connected monitors

- **DPI awareness** for high-resolution displays

- **Cross-monitor selection** - capture areas spanning multiple screens

- **Monitor-specific positioning** for consistent setups



### **Dynamic Capture Areas**

- **Resizable borders** - adjust capture area during operation

- **Movable windows** - reposition without stopping capture

- **Multiple profiles** - save configurations for different applications



### **Text Processing**

- **Duplicate detection** - automatic removal of repeated text

- **Text cleaning** - remove artifacts and formatting issues

- **Processed output** - clean, readable transcriptions



### **Profile Management**

- **Save Settings** - store optimized configurations

- **Quick Load** - switch between saved profiles

- **Application-specific** - different settings for Teams, Zoom, Meet



---



## 💡 Tips & Best Practices



### **Optimizing OCR Accuracy**

- **Language Selection**: Choose the correct language model for best results with accents and special characters

- **Capture Area**: Select narrow, wide rectangles focusing on subtitle regions

- **Minimum Size**: Ensure capture areas are at least 50×50 pixels

- **Stable Areas**: Target regions where text appears consistently



### **Performance Optimization**

- **Close unnecessary applications** to reduce system load

- **Use specific language models** rather than auto-detection

- **Regular cleanup** of old capture files and logs

- **Monitor system resources** during extended capture sessions



---



## 📁 Project Structure



```

CaptiOCR/

├── CaptiOCR.py              # Main application entry point

├── captiocr/                # Core application modules

│   ├── config/              # Settings and constants

│   ├── core/                # OCR and capture logic

│   ├── models/              # Data models

│   ├── ui/                  # User interface components

│   └── utils/               # Utilities and helpers

├── captures/                # Saved text outputs

├── config/                  # User preferences

├── tessdata/                # OCR language files

├── logs/                    # Application logs

└── resources/               # Icons and assets

```



---



## 🔧 Configuration



The application uses JSON configuration files stored in `config/`:

- **User preferences** - UI settings, language choices

- **Language data** - Available OCR models

- **Capture profiles** - Saved area configurations



---



## 📋 System Requirements



- **OS**: Windows 10/11 (primary), Linux/macOS (experimental)

- **RAM**: 4GB minimum, 8GB recommended

- **CPU**: Multi-core processor recommended for real-time processing

- **Display**: Support for multiple monitors with varying DPI

- **Storage**: 100MB+ for application and language files



---



## 🐛 Troubleshooting



### **Common Issues:**

- **OCR not working**: Verify Tesseract installation and PATH

- **Text not detected**: Check language selection and capture area size

- **Performance issues**: Close other applications, check system resources

- **Multi-monitor problems**: Update display drivers, check DPI settings



### **Debug Logging:**

Enable debug logging in the application settings to capture detailed operation information for troubleshooting.



---



## 🗺️ Roadmap



### **Upcoming Features**

- 🔄 **Live translation** integration

- 🔄 **Cloud storage** synchronization  

- 🔄 **Export formats** (PDF, HTML, Word)

- 🔄 **API integration** for external applications

- 🔄 **Dark mode** and theme customization

- 🔄 **Batch processing** capabilities



---



## 🤝 Contributing



We welcome contributions! Here's how to get started:



1. **Fork** the repository

2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)

3. **Follow** the coding guidelines in `CLAUDE.md`

4. **Commit** your changes (`git commit -m 'Add amazing feature'`)

5. **Push** to the branch (`git push origin feature/amazing-feature`)

6. **Open** a Pull Request



### **Development Guidelines**

- Follow **PEP 8** Python style guide

- Use **type hints** and **docstrings**

- Maintain **modular architecture**

- Add **comprehensive logging**

- Update **version numbers** for functional changes



---



## 📄 License



This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.



---



## 👤 Author & Support



**Author:** Carlo Sacchi

**Website:** [https://www.captiocr.com](https://www.captiocr.com)



For support, feature requests, or bug reports, please open an issue on GitHub.



---



**⭐ If CaptiOCR helps you, please consider giving it a star on GitHub!**