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.