Image Processing-Based Playing Card Recognition System

In the field of computer vision, playing card recognition represents a classic image processing application. This system implements a complete card recognition pipeline using MATLAB, capable of accurately identifying both card suits and ranks from images. The workflow consists of several key stages:

The process begins with image preprocessing. Card images typically undergo filtering, noise removal, and edge enhancement operations to improve subsequent processing accuracy. For instance, Gaussian filtering smooths the image while morphological operations eliminate minor noise and enhance the main card contours. In MATLAB implementation, this involves functions like imgaussfilt() for Gaussian filtering and imopen() for morphological opening.

Next comes edge detection and contour extraction. The system employs operators like Canny or Sobel for edge detection, followed by contour analysis to determine card position and orientation within the image. This step is particularly crucial for handling tilted or partially obscured cards, requiring robust algorithm implementation. The edge() function in MATLAB with 'canny' option provides effective edge detection, while bwboundaries() helps extract card contours.

The third stage involves rank and suit recognition. The system typically uses template matching or feature extraction methods (such as HOG or SIFT) to compare segmented rank and suit regions against predefined templates. To enhance recognition accuracy, color space conversion (e.g., RGB to HSV) may be incorporated to better distinguish between red and black suits. MATLAB's normxcorr2() function enables template matching, while extractHOGFeatures() supports feature-based recognition approaches.

Finally, results undergo validation and optimization. Logical verification (e.g., ensuring no duplicate cards exist in a standard deck) reduces misidentification possibilities. The system can adapt to varying lighting conditions through parameter tuning, particularly threshold selection. Implementation involves creating validation rules and using adaptive thresholding techniques like graythresh() for optimal performance across different image qualities.

This system finds applications not only in entertainment scenarios (such as automatic scoring) but also extends to security and automation fields, demonstrating the practical value of image processing technology in pattern recognition applications.