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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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Proceedings ArticleDOI
Handwritten month word recognition on Brazilian bank cheques
TL;DR: An off-line system under development to process unconstrained handwritten dates on Brazilian bank cheques in an omni-writer context and shows improvements on previous work on isolated month word recognition using hidden Markov models (HMM).
Proceedings ArticleDOI
Segmentation of Unideal Iris Images Using Game Theory
TL;DR: This research effort applies a parallel game-theoretic decision making procedure by using the modified Chakra borty and Duncan’s algorithm, which integrates the region-based segmentation and gradient-based boundary finding methods and fuses the complementary strengths of each of these individual methods.
Journal ArticleDOI
Chinese document layout analysis based on adaptive split-and-merge and qualitative spatial reasoning
TL;DR: This paper describes a generic document segmentation and geometric relation labeling method with applications to Chinese document analysis that begins with a hierarchy of partitioned image layers where inhomogeneous higher-level regions are recursively partitioned into lower-level rectangular subregions.
Book ChapterDOI
Dynamic selection of ensembles of classifiers using contextual information
TL;DR: A context-based framework that exploits the internal sources of knowledge embedded in DSA to improve the performance of DSA, and demonstrates that the proposed method can be used, without changing the parameters of the base classifiers, in an incremental learning (IL) scenario, suggesting that it is also a promising general IL approach.
Journal ArticleDOI
Rotation invariant texture classification by ridgelet transform and frequency-orientation space decomposition
TL;DR: A new rotation invariant feature extraction method in the ridgelet transform domain for texture classification is proposed, which can be divided into two stages: the Radon transform stage and the 1-D wavelet transform stage.