Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid Pen-Input Character Recognition System Based on Integration of Online-Offline Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Collection of on-line handwritten Japanese character pattern databases and their analyses
International Journal on Document Analysis and Recognition
Markov Random Fields for Handwritten Chinese Character Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
IEICE - Transactions on Information and Systems
Online Handwritten Japanese Character String Recognition Incorporating Geometric Context
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Segmentation of On-Line Freely Written Japanese Text Using SVM for Improving Text Recognition
IEICE - Transactions on Information and Systems
A robust model for on-line handwritten japanese text recognition
International Journal on Document Analysis and Recognition - Special Issue DRR09
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The paper presents complexity reduction of an on-line handwritten Japanese text recognition system by selecting an optimal off-line recognizer in combination with an on-line recognizer, geometric context evaluation, and linguistic context evaluation. The result is that a surprisingly simple off-line recognizer, which is weak on its own, produces nearly the best recognition rate in combination with other evaluation factors in remarkably small space-and-time complexity. Generally, lower dimensions with fewer principal components produce a smaller set of prototypes, which reduces memory-cost and time-cost. This degrades the recognition rate, however, so we need to reach a compromise. In an evaluation function with the above-mentioned multiple factors combined, the configuration of only 50 dimensions with as few as 5 principal components for the off-line recognizer keeps almost the best accuracy 98.23% (the best accuracy 98.34%) for text recognition while it reduces the total memory-cost to 1/3 (from 99.4MB down to 32MB) and the average time-cost of character recognition for text recognition to 4/5 (from 0.1672ms to 0.1349ms per character) compared with the traditional off-line recognizer with 160 dimensions and 50 principal components.