Reversing ShopView analysis for planogram creation - Telmo Domiciano Pereira Barbosa (2017)
With the increasing care of retail shop owners in improving sales and costumer experience, there is a need to develop technology in order to optimize their goals. It's proven that a planned product placement can boost sales and improve costumer experience [1]. With this in mind, Fraunhofer Portugal came up with ShopView [2] solution to help retail shops extract, validate and manipulate planograms from high-resolution images of the real shelves in the store. In this sense, this thesis focused on the creation of an algorithm using computer vision algorithms to extract information from high resolution images of retail shelves taken with ShopView solution. Particularly, it was implemented pre-processing steps to improve the efficiency and accuracy of an OCR (Optical Character Recognition) engine in recognizing the text in the shelves products. These pre-processing algorithms comprise of denoising and segmentation techniques. The use of this OCR engine brings additional information about products and shelves separation, this information is later used in clustering algorithms to automatically extract an accurate planogram from shelves photos. The presented algorithm is capable of extracting relevant information from the shelves images, to identify the existing products and create a valid metadata about them and their location. With this metadata, it is possible to create, validate and modify the planogram in ShopView. The use of OCR on this algorithm has advantages over other available approaches due to its capability to differentiate products with minimal visual differences and its immunity to appearance changes on the products packaging. Moreover, the methodology proposed does not require any previous user interaction to work properly. [1] Chanjin Chung, Todd M. Schmit, Diansheng Dong, and Harry M. Kaiser. Economic evaluation of shelf-space management in grocery stores. Agribusiness, 23(4):583-597, September 2007. [2] L. Rosado, J. Gonçalves, J. Costa, D. Ribeiro, and F. Soares. Supervised learning for out-of-stock detection in panoramas of retail shelves. In 2016 IEEE International Conference on Imaging Systems and Techniques (IST), pages 406-411.