Point of Sale System Using Convolutional Neural Network for Image Recognition in Grocery Store

Abstract

The history of point of sale already has been told from a long time ago. The business nowadays is opting for the point-of-sale transactions because it was easy to sell the item to people face to face. This will build some trust between the cashier and the customer. The popular store that always customer need was the grocery store. However, the grocery store nowadays still not has a good feature for the point-of-sale system. The cashier still needs to scan the item through barcode scanner. This idea was led to make the point-of-sale transactions easier in the grocery store by applying the machine learning to the system. The problem for this project is the customer wait for a long time for their point-of-sale transactions to finish when bought the grocery items. The aim of this project is to detect the grocery items with convolutional neural network model for image recognition through camera within the main user interface. The Agile Development Life Cycle (ADLC) method is used in the development of Point-of-Sale System using Machine Learning for Image Recognition in Grocery Store. Moreover, this project is to evaluate the usability of the system using Post-Study System Usability Questionnaire (PSSUQ) approach. The PSSUQ evaluation is evaluated by the users of the system. The results of PSSUQ stated that the users satisfied with the system. The future research for this project is to make the point-of-sale system with a better model in the future. In conclusion, the system is works well and machine learning image recognition model also can detect the grocery item clearly.