@inproceedings{f90c714d006b453ebcc6e76cf1f83389,

title = "Handwritten character recognition using a gradient classifier",

abstract = "The authors consider a prototype-based character recognizer that makes comparisons based on blurred representations of the images. The blurring induces a metric on the space of all images that varies continuously under continuous deformations of the image plane. This blurred representation is suitable for direct implementation of a nearest neighbor classifer. However, it is still desirable to have a representation which is invariant under rotation, translation, and scaling of the image plane. A representation which is locally invariant under these transformations is produced by transforming an input to a local minimum of its distance from each prototype simultaneously. These minima are found by performing a gradient descent on an appropriate error surface over the four transformation parameters. The error functional is the L2-norm of the difference between the blurred prototype and the blurred input. The resulting classifier makes more efficient use of prototypes than the nearest neighbor classifier.",

author = "Brandt, {Robert D.} and Yao Wang and Laub, {Alan J.} and Mitra, {Sanjit K.}",

year = "1988",

language = "English (US)",

isbn = "7800030393",

series = "Proc 1988 IEEE Int Conf Syst Man Cybern",

pages = "361--364",

editor = "Anon",

booktitle = "Proc 1988 IEEE Int Conf Syst Man Cybern",

note = "Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics ; Conference date: 08-08-1988 Through 12-08-1988",

}