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W. H. Highleyman

Bio: W. H. Highleyman is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 18 citations.

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Journal ArticleDOI
TL;DR: The state of the art of online handwriting recognition during a period of renewed activity in the field is described, based on an extensive review of the literature, including journal articles, conference proceedings, and patents.
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. >

922 citations

Journal ArticleDOI
TL;DR: H holistic approaches that avoid segmentation by recognizing entire character strings as units are described, including methods that partition the input image into subimages, which are then classified.
Abstract: Character segmentation has long been a critical area of the OCR process. The higher recognition rates for isolated characters vs. those obtained for words and connected character strings well illustrate this fact. A good part of recent progress in reading unconstrained printed and written text may be ascribed to more insightful handling of segmentation. This paper provides a review of these advances. The aim is to provide an appreciation for the range of techniques that have been developed, rather than to simply list sources. Segmentation methods are listed under four main headings. What may be termed the "classical" approach consists of methods that partition the input image into subimages, which are then classified. The operation of attempting to decompose the image into classifiable units is called "dissection." The second class of methods avoids dissection, and segments the image either explicitly, by classification of prespecified windows, or implicitly by classification of subsets of spatial features collected from the image as a whole. The third strategy is a hybrid of the first two, employing dissection together with recombination rules to define potential segments, but using classification to select from the range of admissible segmentation possibilities offered by these subimages. Finally, holistic approaches that avoid segmentation by recognizing entire character strings as units are described.

880 citations

Journal ArticleDOI
George Nagy1
01 Jan 1968
TL;DR: This paper reviews statistical, adaptive, and heuristic techniques used in laboratory investigations of pattern recognition problems and includes correlation methods, discriminant analysis, maximum likelihood decisions minimax techniques, perceptron-like algorithms, feature extraction, preprocessing, clustering and nonsupervised learning.
Abstract: This paper reviews statistical, adaptive, and heuristic techniques used in laboratory investigations of pattern recognition problems. The discussion includes correlation methods, discriminant analysis, maximum likelihood decisions minimax techniques, perceptron-like algorithms, feature extraction, preprocessing, clustering and nonsupervised learning. Two-dimensional distributions are used to illustrate the properties of the various procedures. Several experimental projects, representative of prospective applications, are also described.

317 citations

Journal ArticleDOI
R. O. Duda1, H. Fossum1
TL;DR: This paper describes iterative procedures for determining linear and piecewise linear discriminant functions for multicategory pattern classifiers and shows that this approach compares favorably with other classification methods.
Abstract: This paper describes iterative procedures for determining linear and piecewise linear discriminant functions for multicategory pattern classifiers. While classifiers with the same structure have often been proposed, it is less well known that their parameters can be efficiently determined by simple adjustment procedures. For linear discriminant functions, convergence proofs are given for procedures that are guaranteed to yield error-free solutions on design samples, provided only that such solutions exist. While no similar results are known for piecewise linear discriminant functions, simple procedures are given that have been effective in various experiments. The results of experiments with artificially generated multimodal data and with hand-printed alphanumeric characters are given to show that this approach compares favorably with other classification methods.

156 citations

Journal ArticleDOI
Leon D. Harmon1
01 Oct 1972
TL;DR: The evolution and present state of the art of machine recognition of print and script is examined and major problems of cost and effectiveness still exist.
Abstract: A deceptively simple kind of optical pattern recognition deals with print and script. What seemed at one time to be a fairly easy problem area in automated reading of line-like patterns has turned out to be difficult and expensive. The evolution and present state of the art of machine recognition of print and script is examined. On-hand systems relieve large amounts of human drudgery, and both theory and engineering design have advanced greatly in response to pressures caused by our paper explosion. But major problems of cost and effectiveness still exist.

130 citations