Mobile Vision Testing Framework

A Java-based mobile testing framework that combines Appium with OpenCV and Tesseract OCR for robust visual testing and text recognition in mobile applications.

Features

• Automated mobile app testing using Appium
• Image processing and template matching with OpenCV
• Text recognition using Tesseract OCR
• Support for multiple languages including Hindi
• Configurable image preprocessing for better OCR accuracy
• Detailed logging and debugging capabilities

Prerequisites

• Java JDK 18 or higher
• Maven
• Appium Server 2.0 or higher
• Android SDK (for Android testing)
• Tesseract OCR with language data
• OpenCV (installed via Maven dependency)

Setup

1. Install Tesseract OCR:

brew install tesseract

2. Install language data for Hindi:

brew install tesseract-lang

3. Clone the repository:

git clone https://github.com/sawaii/custom-vision-testing.git
cd custom-vision-testing

4. Install dependencies:

mvn clean install

Configuration

1. Update src/test/resources/config.properties with your Appium settings:

appium.server.url=http://127.0.0.1:4723
appium.device.name=your_device_name
appium.app.path=/path/to/your/app
appium.automation.name=UiAutomator2
appium.no.reset=true
appium.full.reset=false
appium.new.command.timeout=60
appium.auto.grant.permissions=true

Running Tests

Run tests using Maven:

mvn clean test

Run a specific test:

mvn clean test -Dtest=MobileVisionTest#testLanguageSelectionPage

Project Structure

• src/main/java/com/vision/mobile/ - Core framework classes
  • MobileVisionQuery.java - Main class for image processing and OCR
• src/test/java/com/vision/test/ - Test classes
  • MobileVisionTest.java - Example test implementation
• src/test/resources/ - Test resources and configuration files

Features in Detail

Image Processing

• Bilateral filtering for noise reduction
• CLAHE for contrast enhancement
• Custom sharpening kernel
• Otsu's thresholding
• Morphological operations

OCR Capabilities

• Multi-language text recognition
• Region-based text extraction
• Multiple OCR attempts for accuracy
• Configurable preprocessing parameters

Template Matching

• Logo detection
• UI element verification
• Confidence threshold adjustment

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.