Pyramid histogram of oriented gradient for machine-printed/handwritten and Arabic/Latin word discrimination

This paper considers the problem of script and nature identification at word level. We introduce Pyramid Histogram of Oriented Gradients (PHOG) features which have been employed successfully for discriminating between handwritten and machine-printed Arabic and Latin scripts. Most of the image features, used in previous identification system, are not effective to capture differences between these scripts especially due to their cursive nature. The proposed shape descriptor, PHOG features, counts occurrences of gradient orientation in localized portion of an image. It has been proved as an efficient tool for providing spatial distribution of pixels. A genetic algorithm is applied to improve the performance and generalization of the PHOG features. Experiments have been conducted using standard databases. An average identification rate of 98.3 percent was achieved using Bayes based classifier, which is clearly better than those reported in similar works.