Statistical syntactic methods for high-performance OCR

The paper describes a new method for language modelling and reports its application to handwritten OCR. Images of characters are first chain-coded to convert them to strings. A novel language modelling method is then applied to build a statistical model for strings of each class. The language modelling method is based on a probabilistic version of an n-tuple classifier which is scanned along the entire string for both training and recognition. This method is extremely fast and robust, and concentrates all the computational effort on the portion of the image where the information is, i.e. the edges left by the trace of the pen. Results on the CEDAR handwritten digit database show the new method to be almost as accurate as the best methods reported so far, while offering a significant speed advantage.