In the modern digital landscape, QR codes have become a universal medium for storing and transmitting information in fields such as mobile payments, logistics, healthcare, advertising, and electronic documentation. Their main advantage lies in compactness, fast readability, and the ability to store large amounts of data. However, in real-world conditions, QR codes are frequently exposed to distortions including noise, blurring, scratches, masking, geometric warping, or printing errors, which reduce decoding accuracy or make recognition impossible. To overcome these challenges, error correction mechanisms are embedded in QR codes, with Reed–Solomon (RS) coding being the most effective. This study aimed to evaluate the performance of classical detectors (OpenCV, Pyzbar), RS-based correction, and hybrid approaches under artificially induced degradations. A dataset of 100 QR codes was generated, systematically distorted with Gaussian noise, occlusion, blur, scratches, and perspective transformations, and tested for recovery. Results showed that baseline detectors performed well only on mildly degraded codes, with accuracy dropping below 50% in severe cases. RS coding achieved around 79% recovery across all categories, while hybrid approaches integrating preprocessing and RS demonstrated the highest accuracy (≈85%), ensuring robust restoration. The findings confirm the necessity of combining error correction with preprocessing for reliable QR code decoding in practical applications.