Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
On-line Chinese character recognition using ART-based stroke classification
Pattern Recognition Letters
Offline Recognition of Chinese Handwriting by Multifeature and Multilevel Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel principal component analysis
Advances in kernel methods
A Discrete Contextual Stochastic Model for the Offline Recognition of Handwritten Chinese Characters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten Chinese Radical Recognition Using Nonlinear Active Shape Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Offline Handwritten Chinese Character Recognition viaRadical Extraction and Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
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
Hybridization of gradient descent algorithms with dynamic tunnelingmethods for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An online Bayesian Ying-Yang learning applied to fuzzy CMAC
Neurocomputing
Skeleton-Based Recognition of Chinese Calligraphic Character Image
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Using a geometric-based sketch recognition approach to sketch Chinese radicals
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Advanced hough transform using a multilayer fractional fourier method
IEEE Transactions on Image Processing
Offline handwritten Amharic word recognition
Pattern Recognition Letters
Stroke based handwritten character recognition
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Proceedings of the 2012 ACM symposium on Document engineering
LSH-based large scale chinese calligraphic character recognition
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Stroke++: A new Chinese input method for touch screen mobile phones
International Journal of Human-Computer Studies
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Offline handwritten Chinese character recognition is a very hard pattern-recognition problem of considerable practical importance. Two popular approaches are to extract features holistically from the character image or to decompose characters structurally into component parts---usually strokes. Here we take a novel approach, that of decomposing into radicals on the basis of image information (i.e., without first decomposing into strokes). During training, 60 examples of each radical were represented by "landmark" points, labeled semiautomatically, with radicals in different characteristic positions treated as distinctly different radicals. Kernel principal-component analysis then captured the main (nonlinear) variations around the mean radical. During the recognition, the dynamic tunneling algorithm was used to search for optimal shape parameters in terms of chamfer distance minimization. Considering character composition as a Markov process in which up to four radicals are combined in some assumed sequential order, we can recognize complete, hierarchically-composed characters by using the Viterbi algorithm. This gave a character recognition rate of 93.5% characters correct (writer-independent) on a test set of 430,800 characters from 2,154 character classes composed of 200 radical categories, which is comparable to the best reported results in the literature. Although the initial semiautomatic landmark labeling is time consuming, the decomposition approach is theoretically well-motivated and allows the different sources of variability in Chinese handwriting to be handled separately and by the most appropriate means--either learned from example data or incorporated as prior knowledge. Hence, high generalizability is obtained from small amounts of training data, and only simple prior knowledge needs to be incorporated, thus promising robust recognition performance. As such, there is very considerable potential for further development and improvement in the direction of larger character sets and less constrained writing conditions.