Salah Moughyt

Papers

Total Citations

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About

Salah Moughyt is a researcher specializing in computer vision and pattern recognition, with a particular focus on barcode and QR code detection systems. His most cited work, "Remote QR Code Recognition Based on HOG and SVM Classifiers" (2016), has garnered 10 citations and addresses a critical challenge in mobile and industrial applications: accurately recognizing QR codes from a distance. By combining Histogram of Oriented Gradients (HOG) features with Support Vector Machine (SVM) classifiers, Moughyt developed a robust method for detecting and decoding QR codes even when they appear small or distorted in images. This contribution has practical implications for logistics, inventory management, and augmented reality, where reliable long-range scanning is essential. His work stands out for its emphasis on real-world usability, bridging the gap between theoretical computer vision models and deployable commercial solutions. Moughyt’s research continues to influence the development of efficient, low-latency recognition systems, making him a notable figure in applied image processing and automated data capture technologies.