Computation of stereo disparity using regularization
Pattern Recognition Letters
Handwritten numerical recognition based on multiple algorithms
Pattern Recognition
Using Generative Models for Handwritten Digit Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching Using LAT and its Application to Handwritten Numeral Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
A Handwritten Character Recognition System Using Hierarchical Displacement Extraction Algorithm
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Elastic Matching of Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
An off-line signature verification system using an extracted displacement function
Pattern Recognition Letters
Offline Handwritten Chinese Character Recognition Using Optimal Sampling Features
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Analysis and Recognition of Asian Scripts - the State of the Art
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Statistical displacement analysis for handwriting verification
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Recognition-based digitalization of korean historical archives
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
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A recognition system using displacement extraction based on directional features is proposed for handwritten Chinese characters. In the system, after extracting the features from an input image, the displacement is extracted by the minimization of an energy functional, which consists of the Euclidean distance and the smoothness of the extracted displacement. The coarse-to-fine strategy is adopted to escape local minima and reduce computational costs. The statistical classification is performed based on the estimated variance. In addition, the smoothness of the extracted displacement is utilized. An improvement in recognition performance is achieved as compared with the method without displacement extraction.