Character recognition in natural images

This paper tackles the problem of recognizing characters in images of
natural scenes. In particular, we focus on recognizing characters in
situations that would traditionally not be handled well by OCR
techniques. We present an annotated database of images containing
English and Kannada characters. The database comprises of images of
street scenes taken in Bangalore, India using a standard camera. The
problem is addressed in an object cateogorization framework based on a
bag-of-visual-words representation. We assess the performance of
various features based on nearest neighbour and SVM classification. It
is demonstrated that the performance of the proposed method, using as
few as 15 training images, can be far superior to that of commercial
OCR systems. Furthermore, the method can benefit from synthetically
generated training data obviating the need for expensive data
collection and annotation.