A Bayesian Framework for Deformable Pattern Recognition With Application to Handwritten Character Recognition

—Deformable models have recently been proposed for many pattern recognition applications due to their ability to handle large shape variations.These proposed approaches represent patterns or shapes as deformable models, which deform themselves to match with the input image, and subsequently feed the extracted information into a classifier. The three components—modeling, matching, and classification—are often treated as independent tasks. In this paper, we study how to integrate deformable models into a Bayesian framework as a unified approach for modeling, matching, and classifying shapes. Handwritten character recognition serves as a testbed for evaluating the approach. With the use of our system, recognition is invariant to affine transformation as well as other handwriting variations. In addition, no preprocessing or manual setting of hyperparameters (e.g., regularization parameter and character width) is required. Besides, issues on the incorporation of constraints on model f...

• Type: Journal
• Year: 1998
• Where: PAMI
• Authors: Kwok-Wai Cheung, Dit-Yan Yeung, Roland T. Chin
• Keywords: Deformable Models, Large Shape Variations.These, PAMI 1998, Pattern Recognition Applications