Enhancing Efficiency and Speed of an Off-line Classifier Employed for On-line Handwriting Recognition of a Large Character Set

This paper proposes a new approach to accelerating speed and increasing the recognition rate of an off-line recognizer employed for on-line handwriting recognition of Japanese characters. All training patterns are divided according their stroke number to several groups and one single recognizer is dedicated for each group of patterns. Since a number of categories for a single recognizer is smaller, the speed and accuracy improves. First, we make the model of a recognizer and show that our method can theoretically accelerate its recognition speed to 45% of the original time. Then, we employ the method to a practically used off-line recognizer with the result that the recognition rate is increased from 90.73% to 91.60% and the recognition time is reduced to only 49.73% of the original one. Another benefit of our new approach is high scalability so that the recognizer can be optimized for speed and size or for the best accuracy.