1. A method comprising using at least one hardware processor for: training a price tag detector that comprises a gross feature detector and a classifier, to automatically detect a price tag in an image, by: a) training the gross feature detector using a supervised learning process with a set of labeled images, and b) training the classifier using a two-phase hybrid learning process comprising: applying an initial supervised learning phase using the set of labeled images, yielding a semi-trained version of the classifier, and applying a subsequent unsupervised learning phase using a set of unlabeled images, yielding a fully trained version of the classifier, wherein the applying of the unsupervised learning phase comprises, for each unlabeled image: i) detecting multiple price tag hypotheses using the gross feature detector, ii) classifying each price tag hypothesis using the semi-trained classifier, iii) rating each classification based contextual data extracted from the unlabeled image, iv) retraining the semi-trained classifier with the rated classifications, and repeating steps ii) through iv) until the reclassification converges thereby yielding the trained classifier.