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Real-Time Image Processing Application

Overview

An image processing application designed to enhance Optical Character Recognition (OCR) accuracy in real-time. The application leverages asynchronous processing to efficiently analyze dark pixels, connected components, and maximum blob areas in parallel, maintaining a frame rate of 60 FPS.

Features

  • Real-Time Processing: Utilizes asynchronous processing to handle image data in real-time, ensuring minimal latency and high responsiveness.
  • OCR Enhancement: Improves OCR accuracy by analyzing key image components such as dark pixels, connected components, and maximum blob areas.
  • High Performance: Maintains a consistent frame rate of 60 FPS, ensuring smooth and efficient image processing.
  • Qt Framework: Built using the Qt Framework for robust and cross-platform application development.
  • Efficient Analysis: Parallel processing techniques enhance the efficiency and speed of image analysis.

Technologies Used

  • Programming Language: C++
  • Framework: Qt Framework (Qt Core, Qt Widgets, Qt Concurrent, Qt Multimedia)
  • Database: MySQL (modifiable to a No-SQL DB as well)

Installation

Prerequisites

  • Qt Framework: Ensure that the Qt framework is installed on your system. You can download it from here.
  • C++ Compiler: Ensure that a C++ compiler is installed on your system.
  • CMake: Ensure that CMake is installed for building the project.

Steps

  1. Clone the Repository:

    git clone https://github.com/Arup-Chauhan/qt-image-processing-app.git
cd qt-image-processing-app

  2. Build the Project:

    mkdir build
cd build
cmake ..
make

  3. Run the Application:

    ./image-processing-app

Usage

  1. Launching the Application:

    • Run the executable file generated after the build process.

  2. Real-Time Processing:

    • The application will start processing images from the connected camera or video feed in real-time.
    • The frame rate and performance metrics will be displayed on the interface.

  3. Analyzing Images:

    • The application will analyze dark pixels, connected components, and maximum blob areas in each frame.
    • Results will be used to enhance OCR accuracy in real-time.

Performance Metrics

  • Frame Rate: Consistently maintains 60 FPS for smooth real-time processing.
  • OCR Accuracy: Enhances OCR accuracy by efficiently analyzing image components.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any improvements or bug fixes.