This paper introduces a novel high speed convolutional character recognition system. Convolutional mode operation means that no prior localization or segmentation of characters is required, making this mode extremely robust.

The method uses a 2-d n-tuple grid to sample the image, but decomposes the address calculations into two onedimensional scans. This simple innovation leads to a very fast system, and speeds in excess of 100,000 recognitions per second have been achieved for a 10-class character recognition problem, when operated in convolutional mode.

Quantitative performance results show an error rate of 4.3% on the MNist dataset of isolated hand-written characters. Qualitative results are presented on museum archive card images, indicating that the method has great potential for the character recognition component in a document image analysis system for images of this type.