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
Veinerization: a new shape description for flexible skeletonization
TL;DR: The new concept of "veinerization", which produces a graph that contains all the "topological" information needed to derive a wide variety of skeletons, which has been tested on numerous kinds of patterns, including pathological ones like fractal sets well-known for the complexity of their shapes.
Proceedings ArticleDOI
Feature subset selection using genetic algorithms for handwritten digit recognition
TL;DR: Two approaches using genetic algorithms for feature subset selection are compared and it is concluded that the IGA converges faster than the SGA, however, the S GA seems more suitable for the problem.
Journal ArticleDOI
Detecting predatory conversations in social media by deep Convolutional Neural Networks
TL;DR: The novel application of a deep learning method to the automatic identification of predatory chat conversations in large volumes of chat logs is described and a classifier based on Convolutional Neural Network (CNN) is presented to address this problem domain.
Journal ArticleDOI
An alternate smoothing and stripping algorithm for thinning digital binary patterns
Y K Chu,Ching Y. Suen +1 more
TL;DR: An algorithm for thinning digital binary patterns (skeletonization) which alternately smoothing and stripping the pattern and stripping of the contour points produces skeletons which are smooth and with least distortion and well-formed than the others.
Proceedings ArticleDOI
HMM word recognition engine
D. Guillevic,Ching Y. Suen +1 more
TL;DR: A hidden Markov model (HMM) based word recognition engine being developed to be integrated with the CENPARMI bank cheque processing system is described and preliminary results are compared with the previous global feature recognition scheme.