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.
This paper describes a Markov random field
(MRF) model with weighting parameters optimized by
conditional random field (CRF) for on-line
recognition of handwritten Japanese characters. The
model extracts feature points along the pen-tip trace
from pen-down to pen-up and sets each feature point
from an input pattern as a site and each state from a
character class as a label. It employs the coordinates
of feature points as unary features and the differences
in coordinates between the neighboring feature points
as binary features. The weighting parameters are
estimated by CRF or the minimum classification error
(MCE) method. In experiments using the TUAT
Kuchibue database, the method achieved a character
recognition rate of 92.77%, which is higher than the
previous model's rate, and the method of estimating
the weighting parameters using CRF was more
accurate than using MCE.
This paper describes a robust model for on-line handwritten Japanese text recognition. The method evaluates the
likelihood of candidate segmentation paths by combining scores of character pattern size, inner gap, character
recognition, single-character position, pair-character position, likelihood of candidate segmentation point and linguistic
context. The path score is insensitive to the number of candidate patterns and the optimal path can be found by the
Viterbi search. In experiments of handwritten Japanese sentence recognition, the proposed method yielded superior
performance.
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