Techniques for solving the large-scale classification problem in Chinese handwriting recognition

Authors:
Fu Chang
Affiliations:
Institute of Information Science, Academia Sinica, Taipei, Taiwan
Venue:
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Year:
2006

Citing 9
Cited 0

A nonlinear normalization method for handprinted Kanji character recognition—line density equalization

Pattern Recognition
The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Induction of Decision Trees

Machine Learning
High Accuracy Handwritten Chinese Character Recognition by Improved Feature Matching Method

ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Applying A Hybrid Method To Handwritten Character Recognition

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A prototype classification method and its use in a hybrid solution for multiclass pattern recognition

Pattern Recognition
Accelerating feature-vector matching using multiple-tree and sub-vector methods

Pattern Recognition
Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Given the large number of categories, or class types, in the Chinese language, the challenge offered by character recognition involves dealing with such a large-scale problem in both training and testing phases. This paper addresses three techniques, the combination of which has been found to be effective in solving the problem. The techniques are: 1) a prototype learning/matching method that determines the number and location of prototypes in the learning phase, and chooses the candidates for each character in the testing phase; 2) support vector machines (SVM) that post-process the top-ranked candidates obtained during the prototype learning or matching process; and 3) fast feature-vector matching techniques to accelerate prototype matching via decision trees and sub-vector matching. The techniques are applied to Chinese handwritten characters, expressed as feature vectors derived by extraction operations, such as nonlinear normalization, directional feature extraction, and feature blurring.