Recognition of Grocery Products in Images captured by Cellular Phones

Einsele, Farshideh; Foroosh, Hassan (2015). Recognition of Grocery Products in Images captured by Cellular Phones International Journal of Computer and Information Engineering, 9(1), pp. 159-163. World Academy of Science

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In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation, style, illumination, and can suffer from perspective distortion. Pre-processing is performed to make the characters scale and rotation invariant. Since text degradations can not be appropriately defined using well-known geometric transformations such as translation, rotation, affine transformation and shearing, we use the whole character black pixels as our feature vector. Classification is performed with minimum distance classifier using the maximum likelihood criterion, which delivers very promising Character Recognition Rate (CRR) of 89%. We achieve considerably higher Word Recognition Rate (WRR) of 99% when using lower level linguistic knowledge about product words during the recognition process.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	Business School > Business Foundations and Methods > Digital Lab Business School
Name:	Einsele, Farshideh and Foroosh, Hassan
Series:	Engineering and Technology
Publisher:	World Academy of Science
Submitter:	Service Account
Date Deposited:	21 Oct 2019 11:32
Last Modified:	21 Oct 2019 11:32
Uncontrolled Keywords:	Camera-based OCR, Feature extraction, Document and image processing
ARBOR DOI:	10.24451/arbor.7749
URI:	https://arbor.bfh.ch/id/eprint/7749