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
More filters
Journal ArticleDOI
A thinning algorithm based on the force between charged particles
TL;DR: A new thinning algorithm based on the well known concept of the force of attraction or repulsion between charged particles is presented and is experimentally compared with four other known algorithms published in the literature.
Journal ArticleDOI
A tree conditional random field model for panel detection in comic images
TL;DR: This work proposes a novel context modeling method that incorporates three types of visual patterns extracted from the comic image at different levels and a tree conditional random field framework is used to label each visual pattern by modeling its contextual dependencies.
Un système neuro-flou pour la reconnaissance de montants numériques de chèques arabes
TL;DR: In this paper, a systeme de reconnaissance de chaines de chiffres indiens dedie a la lecture automatique des montants numeriques de cheques arabes Saoudiens is presented.
Journal ArticleDOI
Recognition of handwritten characters by parts with multiple orientations
TL;DR: This study displays a deeper, inherent similarity, and distinctness among different patterns and characters, which include part symmetry and part resemblance in different possible positions, which should be useful to pattern analysis and recognition.
Book ChapterDOI
Objective Evaluation of the Discriminant Power of Features in an HMM-based Word Recognition System
TL;DR: An elegant method for evaluating the discriminant power of features in the framework of an HMM-based word recognition system that employs statistical indicators, entropy and perplexity, to quantify the capability of each feature to discriminate between classes without resorting to the result of the recognition phase.