  Retail checkout systems employed at supermarkets primarily rely on barcode
scanners, with some utilizing QR codes, to identify the items being purchased.
These methods are time-consuming in practice, require a certain level of human
supervision, and involve waiting in long queues. In this regard, we propose a
system, that we call ARC, which aims at making the process of check-out at
retail store counters faster, autonomous, and more convenient, while reducing
dependency on a human operator. The approach makes use of a computer
vision-based system, with a Convolutional Neural Network at its core, which
scans objects placed beneath a webcam for identification. To evaluate the
proposed system, we curated an image dataset of one-hundred local retail items
of various categories. Within the given assumptions and considerations, the
system achieves a reasonable test-time accuracy, pointing towards an ambitious
future for the proposed setup. The project code and the dataset are made
publicly available.
