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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
Combination of spatially enhanced bag-of-visual-words model and genuine difference subspace for fake coin detection
TL;DR: An intelligent system to automatically detect fake coins based on their images is presented and a new spatially enhanced bag-of-visual-words model, called SEBOVW model, is proposed, which is trained to discriminate between genuine and fake coins.
Book ChapterDOI
Scale and Rotation Invariant Character Segmentation from Coins
TL;DR: This paper transforms the coin from circular into rectangular shape and then performs morphological operations to compute the horizontal and vertical projection profiles and apply dynamic adaptive mask to extract characters and achieves precision and recall rates as high as 93.5% and 94.8% respectively demonstrating the effectiveness of the proposed method.
Journal ArticleDOI
Parallel regional projection transformation (RPT) and VLSI implementation
TL;DR: A new regional projection transformation to recognize unconnected patterns and patterns with isolated noise is presented, which simplifies the process of recognizing compound patterns by transforming them into an integral object.
Journal ArticleDOI
Gap metrics for handwritten Korean word segmentation
TL;DR: A gap-based method for the segmentation of handwritten Korean text lines into separate words is described, and three kinds of gap metrics have been evaluated to see how much the gap information contributes to the task of handwriting Korean word segmentation.
Book ChapterDOI
Rotation-Invariant texture classification using steerable gabor filter bank
TL;DR: An efficient rotation invariant feature extraction technique for texture classification based on Gabor multi-channel filtering is proposed, where Gabor function is approximated by a set of steerable basis functions, which results in a significant saving in the computation cost.