This paper presents a system that overcomes the dependence on pattern transformation, like translation, rotation, scaling and further deformations of the input to a recognition system, by reducing the pattern to a normal form. The reduction may be viewed as pre-processing that uses different algorithms to reduce the pattern to normal form at: 0, 1, 2, .., n-level. Our system performs, on patterns representing binary images of characters, the reduction to a normal pattern of level 0, 1 and 2, that in practice correspond, respectively, to character extraction, scaling and rotation until the recovery of a standard condition for these. The patterns so normalised are supplied as input to a recognition system, constituted by a Hintzman neural network, that is a content-addressable-memory, which has well known problems of sensitivity to the input variations.
Using normal patterns in handwritten character recognition
01.01.1999
72234 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Using Normal Patterns in Handwritten Character Recognition
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