Deep Learning in POS Automation: Challenges, Innovations & Trends

Deep learning has revolutionized computer vision, driving automation in industries like retail. Artificial Intelligence (AI) powered object recognition is transforming Point-of-Sale (POS) automation, improving transaction speed, accuracy, and efficiency. However, challenges remain, such as barcode recognition in difficult conditions and optimizing AI models for real-time performance.This book explores how deep learning enhances POS automation, focusing on barcode recognition and discount sale identification. It covers the evolution of POS systems, AI's role in retail, and advanced barcode detection techniques, including deep learning models and synthetic-to-real data augmentation. The book also discusses AI-driven discount sale recognition, system design, and experimental results.Beyond technical solutions, the book addresses deployment challenges, such as hardware integration, balancing cloud and edge AI processing, and optimizing models for real-time use. It also looks at emerging trends like self-supervised learning, domain adaptation, and applications in logistics and automated warehouses.

Autorentext
Dr. Xiaoyan DaiPh.D. in image processing from Muroran Institute of Technology, Japan. After working at Canon Inc., she joined Kyocera's Research Center as an Expert. Her research focuses on AI and computer vision, including object detection and fraud detection. She is an active committee member and speaker at multiple international conferences.