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• W2110424630 abstract "This paper presents a new approach to representation and recognition of handwritten numerals. The approach first transforms a two-dimensional (2-D) spatial representation of a numeral into a three-dimensional (3-D) spatio-temporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. A multiresolution critical-point segmentation method is then proposed to extract local feature points, at varying degrees of scale and coarseness. A new neural network architecture, referred to as radial-basis competitive and cooperative network (RCCN), is presented especially for handwritten numeral recognition. RCCN is a globally competitive and locally cooperative network with the capability of self-organizing hidden units to progressively achieve desired network performance, and functions as a universal approximator of arbitrary input-output mappings. Three types of RCCNs are explored: input-space RCCN (IRCCN), output-space RCCN (ORCCN), and bidirectional RCCN (BRCCN). Experiments against handwritten zip code numerals acquired by the U.S. Postal Service indicated that the proposed method is robust in terms of variations, deformations, transformations, and corruption, achieving about 97% recognition rate." @default.
• W2110424630 created "2016-06-24" @default.
• W2110424630 creator A5053195374 @default.
• W2110424630 creator A5060268420 @default.
• W2110424630 date "1996-03-01" @default.
• W2110424630 modified "2023-09-24" @default.
• W2110424630 title "Unconstrained handwritten numeral recognition based on radial basis competitive and cooperative networks with spatio-temporal feature representation" @default.
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• W2110424630 doi "https://doi.org/10.1109/72.485680" @default.
• W2110424630 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18255598" @default.
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