English
资源说明：Online handwritten Chinese text recognition
(OHCTR) is a challenging problem as it involves a large-scale
character set, ambiguous segmentation, and variable-length input
sequences. In this paper, we exploit the outstanding capability
of path signature to translate online pen-tip trajectories into
informative signature feature maps using a sliding window-based
method, successfully capturing the analytic and geometric
properties of pen strokes with strong local invariance and
robustness. A multi-spatial-context fully convolutional recurrent
network (MC-FCRN) is proposed to exploit the multiple spatial
contexts from the signature feature maps and generate a
prediction sequence while completely avoiding the difficult
segmentation problem. Furthermore, an implicit language model
is developed to make predictions based on semantic context
within a predicting feature sequence, providing a new perspective
for incorporating lexicon constraints and prior knowledge about
a certain language in the recognition procedure. Experiments on
two standard benchmarks, Dataset-CASIA and Dataset-ICDAR,
yielded outstanding results, with correct rates of 97.10% and
97.15%, respectively, which are significantly better than the best
result reported thus far in the literature.
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