AI Based Automatic Billing System

Authors

  • Varsha Kshirsagar Assistant Professor, Department of EEE, RMD Sinhgad School of Engineering., Warje, Pune, Maharashtra, India. Author
  • Pratik Nagare UG Scholar, Department of EEE, RMD Sinhgad School of Engineering., Warje, Pune, Maharashtra, India. Author
  • Atharv Kharche UG Scholar, Department of EEE, RMD Sinhgad School of Engineering., Warje, Pune, Maharashtra, India. Author
  • Arpita Koralli UG Scholar, Department of EEE, RMD Sinhgad School of Engineering., Warje, Pune, Maharashtra, India. Author

DOI:

https://doi.org/10.47392/IRJAEM.2025.0395

Keywords:

Automatic Billing System, Raspberry Pi 3ModelB+, Load Cell, Camera Module, Hx711, Picamera, TensorFlow Lite, Flask, Edge Impulse, Fruit Detection

Abstract

Over the past few years, demand for quick, accurate, and transparent billing mechanisms has developed exponentially across many sectors. Conventional billing systems tend to falter under lengthy wait times, manual mistakes, and inefficiencies, particularly in busy settings like retail outlets and utility services. This paper presents Auto Bill, a machine learning-based automatic billing platform that uses computer vision, machine learning, and embedded systems to automatically identify products, calculate prices, and process payments. Auto Bill uses sophisticated image recognition techniques in combination with weight sensors to scan and analyse products in real-time. Although tailored initially for use in retail applications, the system also finds application in areas like energy and water utilities, where it identifies consumption patterns and produces precise bills from real-time information. Mobile payments and web portals provide extended customer interaction by presenting customers with real-time billing details and transaction records. Auto Bill also supports contactless billing, which is in line with health and safety needs of today's world. This article covers the design, development, and impact of Auto Bill on the industry, noting its efficiency to enhance operation, customer satisfaction, and business decision making based on data.

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Published

2025-07-25