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.

An approach to the representation of signatures in an offline environment that makes a tracing of a signature in a manner similar to that of a human normally does, and incorporates the dynamic information of the tracing sequence into the representation of the signature, is presented. Tracing involves hierarchical decision-making for stroke identification and ordering based on a set of heuristic rules. These heuristic rules work as operators that transform a two-dimensional (2-D) spatial pattern into a one-dimensional (1-D) temporal pattern, thus making it possible to extract the dynamic features of a signature. Following the stroke sequence of a signature identified by the tracing, a multiresolution critical-point segmentation method is proposed to extract local feature points, at varying degrees of scale and coarseness, for representation. To make the representation translation-, rotation-, and scaling-invariant, a critical-point normalization method is introduced. Experimental results are shown.>

An approach for representing signatures in an offline environment is presented. The approach first makes a tracing of a signature similar to the way a human normally does, using hierarchical decision-making for stroke identification and ordering based on a set of heuristic rules. The dynamic information from the tracing sequence is then incorporated into the representation of the signature. A multiresolution critical-point segmentation method is used to extract local feature points, at varying degrees of scale and coarseness, for subsequent representation of the signature. Experimental results are discussed.>

An approach to the representation of arbitrary line drawings is presented. With this approach the system makes a tracing of line drawings similar to the way a human normally does, and incorporates the result of the tracing sequence into the structural representation of the line drawing. Following the sequence of line drawings identified by the tracing, the critical point segmentation method is proposed to extract all the local feature points for representation. To make the representation size and rotation invariant, the authors introduce the critical point normalization method based on orthonormal transformation. Experimental results are shown.>