The introduction of artificial intelligence techniques in the retail market is making a revolution in shopping experience. It allows shoppers to walk into a store, grab what they want and simply walk out without scanning barcodes or having to stand in long queues. That is what we call cashierless stores. In this project, it aims to provide an efficient solution to the automatic retail product identification. This solution presents an artifact one can use to build an end-to-end smart system for cashierless stores. Henceforth, a solution based on text classification is proposed to recognize and identify the products. For that, deep learning techniques are used such as RNN and LSTM to build the classifier. The performance of this classifier is evaluated using various metrics and it shows its efficiency with an accuracy exceeding 86%.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hj-52659 |
| Date | January 2021 |
| Creators | Zhong, Shiting |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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