Billing System using Machine Learning

Abstract

Abstract: The system of using pre-made bar codes to identify a product during its billing process is time-consuming and labour intensive. The relatively unique barcode needs to be first produced, then it must be manually attached to the product. This requires a lot of pre-processing work on the products to make them ready for identification and classification. This paper presents an alternate system that works on the principle of using the products’ natural characteristics like its discrete and distinguishable looks to identify and classify them during the billing process. It’s mimicking the human way of identifying and distinguishing the products. To implement this system, we need to move away from conventional methods of programming and use a different paradigm for designing software systems based on an artificial intelligence concept i.e., machine learning. We use machine learning techniques to design the working philosophy of this system. The algorithms in Deep Neural Networks which is a part of Artificial Neural Networks, help in creating a model to base our software system’s operational logic. Especially the models based on Convolutional Neural Networks have been proven to be efficient in providing a model for image classification. This paper discusses the abstracted software system from the base billing process without worrying about the hardware environment. We choose Python and its web framework Django to design the UI to implement this system over a distributed network within any establishment that needs to incorporate this process so that each node that has to process billing need not have to adhere to the hardware requirements imposed on them to run the various CNN models which are reliant on the GPU-based tensor architecture of TensorFlow. The system also provides mechanisms for inventory management over distributed networks and simple data analytics based on local sales.