A Dynamic Programming Approach to Sequential Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special memorial issue for Professor King-Sun Fu
Shape recognition by integrating structural descriptions and geometrical/statistical transforms
Computer Vision and Image Understanding
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
OCR in a Hierarchical Feature Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Network Modeling of Strokes and their Relationships for On-line Handwriting Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Inference and Learning in Hybrid Bayesian Networks
Inference and Learning in Hybrid Bayesian Networks
Learning probabilistic networks
The Knowledge Engineering Review
Online Handwritten Shape Recognition Using Segmental Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective handwritten hangul recognition method based on the hierarchical stroke model matching
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Off-line cursive script recognition: current advances, comparisons and remaining problems
Artificial Intelligence Review
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In structural character recognition, a character is usually viewed as a set of strokes and the spatial relationships between them. Therefore, strokes and their relationships should be properly modeled for effective character representation. For this purpose, we propose a modeling scheme by which strokes as well as relationships are stochastically represented by utilizing the hierarchical characteristics of target characters. A character is defined by a multivariate random variable over the components and its probability distribution is learned from a training data set. To overcome difficulties of the learning due to the high order of the probability distribution (a problem of curse of dimensionality), the probability distribution is factorized and approximated by a set of lower-order probability distributions by applying the idea of relationship decomposition recursively to components and subcomponents. Based on the proposed method, a handwritten Hangul (Korean) character recognition system is developed. Recognition experiments conducted on a public database show the effectiveness of the proposed relationship modeling. The recognition accuracy increased by 5.5 percent in comparison to the most successful system ever reported.