Showing papers by "Ching Y. Suen published in 1990"

The state of the art in online handwriting recognition

Abstract: This survey describes the state of the art of online handwriting recognition during a period of renewed activity in the field. It is based on an extensive review of the literature, including journal articles, conference proceedings, and patents. Online versus offline recognition, digitizer technology, and handwriting properties and recognition problems are discussed. Shape recognition algorithms, preprocessing and postprocessing techniques, experimental systems, and commercial products are examined. >

Complementary algorithms for the recognition of totally unconstrained handwritten numerals

Abstract: Two novel methods for recognizing totally unconstrained handwritten numerals are presented. One classifies samples based on structural features extracted from their skeletons; the other makes use of their contours. Both methods achieve high recognition rates (86.05%, 93.90%) and low substitution rates (2.25%, 1.60%). To take advantage of the inherent complementarity of the two methods, different ways of combining them are studied. It is shown that it is possible to reduce the substitution rate to 0.70%, while the recognition rate remains as high as 92.00% . Furthermore, if reliability is of utmost importance, one can avoid substitutions completely (reliability 100%) and still retain a fairly high recognition rate (84.85%). >

Verifying, validating, and measuring the performance of expert systems

Abstract: The use of expert systems has increased rapidly during the last few years. There is a growing need for systematic and reliable techniques for evaluating both expert system shells and complete expert systems. In this paper, we discuss evaluation strategies from several points of view: classification, validation, verification, and performance analysis. We note that ther are several respects in which expert system evaluation is similar to software evaluation in general and, consequently, that it may be possible to apply established software engineering techniques to expert system evaluation. In particular, formal analysis is replacing (or enhancing) traditional testing of conventional software. We believe that increasing formalization is an important trend and we indicate ways in which it could be carried further.

A generalized knowledge-based system for the recognition of unconstrained handwritten numerals

Abstract: A method of recognizing unconstrained handwritten numerals using a knowledge base is proposed. Features are collected from a training set and stored in a knowledge base that is used in the recognition stage. Recognition is accomplished by either an inference process or a structural method. The scheme is general, flexible, and applicable to different methods of feature extraction and recognition. By changing the acceptance parameters, a continuous range of performance can be achieved. Encouraging results on nearly 17000 totally unconstrained handwritten numerals are presented. The performance of the system under different recognition-rejection tradeoff ratios is analyzed in detail. >

Splitting-shooting methods for nonlinear transformations of digitized patterns

Abstract: New splitting-shooting methods are presented for nonlinear transformations T: ( xi , eta ) to (x,y) where x=x( xi , eta ), y=y( xi , eta ). These transformations are important in computer vision, image processing, pattern recognition, and shape transformations in computer graphics. The methods can eliminate superfluous holes or blanks, leading to better images while requiring only modest computer storage and CPU time. The implementation of the proposed algorithms is simple and straightforward. Moreover, these methods can be extended to images with gray levels, to color images, and to three dimensions. They can also be implemented on parallel computers or VLSI circuits. A theoretical analysis proving the convergence of the algorithms and providing error bounds for the resulting images is presented. The complexity of the algorithms is linear. Graphical and numerical experiments are presented to verify the analytical results and to demonstrate the effectiveness of the methods. >

Nonlinear shape restoration by transformation models

Abstract: An entropy-reduced transformation (ERT) approach to nonlinear shape restoration has been developed. Nonlinear shape distortions are formulated using nonlinear shape transformations derived from the finite-element theory. Several algorithms which perform the nonlinear shape transformations are given. The inverse nonlinear shape transformation algorithms are described. Some application experiments are described, and results are given. >

Shape transformation models and their applications in pattern recognition

Abstract: This paper presents linear and bilinear shape transformations including basic transformations, analyzes their geometric properties, and provides computer algorithms. The shape transformations can be used to simplify the recognition of Roman letters, Chinese characters and other pictorial patterns by normalizing their shapes to the standard forms. Important theoretical analyses have been performed to illustrate that the linear and bilinear transformations are applicable to computer recognition of digitized patterns. A number of pictorial examples have been computed to confirm the analyses and conclusions made.

A comparative study of nonlinear shape models for digital image processing and pattern recognition

Abstract: Four nonlinear shape models are presented: polynomial, Coons, perspective, and projective modes. Algorithms and some properties of these models are provided. For a given physical model, such as a perspective model, comparisons are made with other mathematical models. It is proved that, under certain conditions, the perspective models can be replaced by the Coons models. Problems related to substitution and approximation of practical models that facilitate digital image processing are raised and discussed. Experimental results on digital images are presented. >

Harmonic models of shape transformations in digital images and patterns

Abstract: A harmonic model of shape transformations is presented. In this model, harmonic functions are governed by the Laplace equation. This model can convert all image or a pattern to another with arbitrary shapes. The transformation process is harmonic, without abruptness and discontinuity. This model can be used to generate and recognize handwritten Roman letters and Chinese characters, fingerprints, and other types of digitized images and patterns. The algorithms of the harmonic models involve partial differential equations and their numerical solutions. Therefore, an intrinsic link between pattern images and numerical methods is presented. >