OCR in a hierarchical feature space

This paper describes a methodology that allows fast and accurate character recognition while keeping the dimensionality of the feature space relatively small. Higher dimensionality can add to the discriminatory power of a recognizer but pays the price in an increase of computational time. We present a method that achieves high accuracy even with a low-dimensional feature space by simulating a multi-resolution feature space. Our approach is supported by promising experimental results. Recognition rate of 98% is achieved on a test set of about 16,000 handwritten numerals. Recognition rates on upper and lower case handprinted characters is about 95%.