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:

Department of Business
Department of Business > Abteilung Methoden und Grundlagen > Methods and tools/Digital lab

Name:

Einsele, Farshideh and Foroosh, Hassan

Series:

Engineering and Technology

Publisher:

World Academy of Science

Submitter:

Admin import user

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

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