A Step Towards Unification of Syntactic and Statistical Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special memorial issue for Professor King-Sun Fu
On the Recognition of Printed Characters of Any Font and Size
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical character recognition by the method of moments
Computer Vision, Graphics, and Image Processing
Separating similar complex Chinese characters by Walsh transform
Pattern Recognition
On machine recognition of hand-painted Chinese characters by feature relaxation
Pattern Recognition
Recognition of Handwritten Chinese Characters by Modified Hough Transform Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Character recognition—a review
Pattern Recognition
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Constraint-Based Searching: Algorithms and Architectures
Constraint-Based Searching: Algorithms and Architectures
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust shape retrieval through a novel statistical descriptor
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
| Hi-index | 0.14 |
The constraint graph is introduced as a general character representation framework for recognizing multifont, multiple-size Chinese characters. Each character class is described by a constraint graph model. Sampling points on a character skeleton are taken as nodes in the graph. Connection constraints and position constraints are taken as arcs in the graph. For patterns of the same character class, the model captures both the topological invariance and the geometrical invariance in a general and uniform way. Character recognition is then formulated as a constraint-based optimization problem. A cooperative relaxation matching algorithm that solves this optimization problem is developed. A practical optical character recognition (OCR) system that is able to recognize multifont, multiple-size Chinese characters with a satisfactory performance was implemented.