Handwritten Chinese character recognition using nonlinear active shape models and the Viterbi algorithm

Authors:
Daming Shi;Steve R. Gunn;Robert I. Damper
Affiliations:
Department of Electronics and Computer Science, Image, Speech and Intelligent Systems (ISIS) Research Group, University of Southampton, Southampton S017 1B J, UK;Department of Electronics and Computer Science, Image, Speech and Intelligent Systems (ISIS) Research Group, University of Southampton, Southampton S017 1B J, UK;Department of Electronics and Computer Science, Image, Speech and Intelligent Systems (ISIS) Research Group, University of Southampton, Southampton S017 1B J, UK
Venue:
Pattern Recognition Letters
Year:
2002

Citing 10
Cited 4

Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
A transformation invariant matching algorithm for handwritten Chinese character recognition

Pattern Recognition
One-Pass Parallel Thinning: Analysis, Properties, and Quantitative Evaluation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application

Computer Vision and Image Understanding
Non-linear point distribution modelling using a multi-layer perceptron

BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Kernel principal component analysis

Advances in kernel methods
Recognition-based handwritten Chinese character segmentation using a probabilistic Viterbi algorithm

Pattern Recognition Letters
Dynamic Programming

Dynamic Programming
Active Radical Modeling for Handwritten Chinese Characters

ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Complex character decomposition using deformable model

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Nonlinear Active Handwriting Models and Their Applications to Handwritten Chinese Radical Recognition

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Offline handwritten Chinese character recognition by radical decomposition

ACM Transactions on Asian Language Information Processing (TALIP)
Advanced hough transform using a multilayer fractional fourier method

IEEE Transactions on Image Processing
A handwritten Bangla numeral recognition scheme based on expanded two-layer SOM

International Journal of Intelligent Systems Technologies and Applications

Quantified Score

Hi-index	0.10

Visualization

Abstract

Since Chinese characters are composed from a small set of fundamental shapes (radicals) the problem of recognising large numbers of characters can be converted to that of extracting a small number of radicals and then finding their optimal combination. In this paper, radical extraction is carried out by nonlinear active shape models, in which kernel principal component analysis is employed to capture the nonlinear variation. Treating Chinese character composition as a discrete Markov process, we also propose an approach to recognition with the Viterbi algorithm. Our initial experiments are conducted on off-line recognition of 430,800 loosely-constrained characters, comprised of 200 radical categories covering 2154 character categories from 200 writers. The correct recognition rate is 93.5% characters correct (writer-independent). Consideration of published figures for existing radical approaches suggests that our method achieves superior performance.