C
Ching Y. Suen
Researcher at Concordia University
Publications - 532
Citations - 25017
Ching Y. Suen is an academic researcher from Concordia University. The author has contributed to research in topics: Handwriting recognition & Feature extraction. The author has an hindex of 65, co-authored 511 publications receiving 23594 citations. Previous affiliations of Ching Y. Suen include École de technologie supérieure & Concordia University Wisconsin.
Papers
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Journal ArticleDOI
An evaluation of parallel thinning algorithms for character recognition
Lily Lam,Ching Y. Suen +1 more
TL;DR: The performance of 10 parallel thinning algorithms from this perspective is reported on by gathering statistics from their performance on large sets of data and examining the effects of the differentthinning algorithms on an OCR system.
Journal ArticleDOI
Building a new generation of handwriting recognition systems
TL;DR: An assessment of the current state of the art in handwriting recognition is given and some evidences and novel ideas on ways of stretching the limits of handwriting recognition systems aiming at outperforming human beings are presented.
Proceedings ArticleDOI
Feature selection using multi-objective genetic algorithms for handwritten digit recognition
TL;DR: The use of genetic algorithms for feature selection for handwriting recognition where sensitivity analysis and neural networks are employed to allow the use of a representative database to evaluate fitness and a validation database to identify the subsets of selected features that provide a good generalization.
Journal ArticleDOI
Dynamic selection approaches for multiple classifier systems
TL;DR: DSAc dominated DSAm on most problems, showing that the use of contextual information can reach better performance than other existing methods and that dynamic selection is generally preferred over static approaches when the recognition problem presents a high level of uncertainty.
Journal ArticleDOI
Analysis of class separation and combination of class-dependent features for handwriting recognition
TL;DR: A new approach to combine multiple features in handwriting recognition based on two ideas: feature selection-based combination and class dependent features that are effective in separating pattern classes and the new feature vector derived from a combination of two types of such features further improves the recognition rate.