  We propose a novel solution for the cashier problem. Current cashier
system/Point of Sale (POS) terminals can be inefficient, cumbersome and
time-consuming for the users. There is a need for a solution dependent on
modern technology and ubiquitous computing resources. We present I-POST
(Intelligent Point of Sale and Transaction) as a software system that uses
smart devices, mobile phone and state of the art machine learning algorithms to
process the user transactions in automated and real time manner. I-POST is an
automated checkout system that allows the user to walk in a store, collect his
items and exit the store. There is no need to stand and wait in a queue. The
system uses object detection and facial recognition algorithm to process the
authentication of the client and the state of the object. At point of exit, the
classifier sends the data to the backend server which execute the payments. The
system uses Convolution Neural Network (CNN) for the image recognition and
processing. CNN is a supervised learning model that has found major application
in pattern recognition problem. The current implementation uses two classifiers
that work intrinsically to authenticate the user and track the items. The model
accuracy for object recognition is 97%, the loss is 9.3%. We expect that such
systems can bring efficiency to the market and has the potential for broad and
diverse applications.
