Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2

Publications

2 Recognition of Grocery Products in Images Captured by Cellular Phones

Authors: Farshideh Einsele, Hassan Foroosh

Abstract:

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.

Keywords: Feature Extraction, camera-based OCR, Document and image processing

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1 A Study of the Variability of Very Low Resolution Characters and the Feasibility of Their Discrimination Using Geometrical Features

Authors: Farshideh Einsele, Rolf Ingold

Abstract:

Current OCR technology does not allow to accurately recognizing small text images, such as those found in web images. Our goal is to investigate new approaches to recognize very low resolution text images containing antialiased character shapes. This paper presents a preliminary study on the variability of such characters and the feasibility to discriminate them by using geometrical features. In a first stage we analyze the distribution of these features. In a second stage we present a study on the discriminative power for recognizing isolated characters, using various rendering methods and font properties. Finally we present interesting results of our evaluation tests leading to our conclusion and future focus.

Keywords: world wide web, Optical Character Recognition, Document Analysis, pattern recognition

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