Exemplar Driven Character Recognition in the Wild

Character recognition in natural scenes continues to represent a formidable challenge in computer vision. Traditional optical character recognition (OCR) methods fail to perform well on characters from scene text owing to a variety of difficulties in background clutter, binarisation, and arbitrary skew. Further, English characters group into only 62 classes whereas many of the world’s languages have several hundred classes. In particular, most Indic script languages such as Kannada exhibit large intra class diversity, while the only difference between two classes may be in a minor contour above or below the character. These considerations motivate an exemplar approach to classification; one which does not seek intra class commonality among extreme examples which are essentially sub classes of their own. Exemplar SVM’s have been recently introduced in the object recognition context. The essence of the exemplar approach is that rather than seeking to establish commonality within classes, a separate classifier is learnt for each exemplar in the dataset. To make individual classification simple, linear SVM’s are used and each classifier is hence an exemplar specific weight vector. Each exemplar in the dataset is resized to standard dimensions, and thence HOG features are densely extracted to create a rigid template xE . A set of negative samples NE are created by the same process from classes not corresponding to the exemplar. Each classifier (wE ,bE ) maximizes the separation between xE and every window in NE . This is equivalent to optimizing the convex objective[4]: