Multiscale Histogram of Oriented Gradient Descriptors for Robust Character Recognition

Characters extracted from images or graphics pose a challenge for traditional character recognition techniques. The high degree of intraclass variation along with the presence of clutter makes accurate recognition difficult, yet the semantic information conveyed by sections of text within images or graphics makes their recognition an important problem. Previous work has shown that, on the two most commonly used datasets of such characters, Histogram of Oriented Gradient (HOG) descriptors have outperformed other methods. In this work we consider two extensions of the HOG descriptor to include features at multiple scales, and evaluate their performance using characters taken from images and graphics. We demonstrate that, by combining pairs of oriented gradients at different scales, it's possible to achieve an increase in performance of 12.4% and 5.6% on the two datasets.