Vector Quantisation Classi ers for Handwritten Character
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چکیده
The development of a pattern recognition architecture based on vector quantization techniques is presented which is applied to the recognition of handwritten bank forms. After an overview of nearest-neighbor classiication and clustering, a fast completely binary version of the k-means algorithm is introduced and results for large character databases are given. An integration of these methods in a multi-agent environment is discussed. Both the eecient implementation on general MIMD processors and a realization on a dedicated SIMD architecture are presented.
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تاریخ انتشار 1995