Complexity reduction with recognition rate maintained for online handwritten Japanese text recognition

Abstract

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 small off-line recognizer, which alone is weak, produces nearly the best recognition rate in combination with other evaluation factors in remarkably small space and time complexity. Generally speaking, lower dimensions with less principle components produce a smaller set of prototypes, which reduce memory-cost and time-cost. It degrades the recognition rate, however, so that we need to compromise them. In an evaluation function with the above-mentioned multiple factors combined, the configuration of only 50 dimensions with as little as 5 principle components for the off-line recognizer keeps almost the best accuracy 97.87% (the best accuracy 97.92%) for text recognition while it suppresses the total memory-cost from 99.4 MB down to 32 MB and the average time-cost of character recognition for text recognition from 0.1621 ms to 0.1191 ms compared with the traditional offline recognizer with 160 dimensions and 50 principle components.