23,110 research outputs found
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
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 (MCFCRN) 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.Comment: 14 pages, 9 figure
Implicit Language Model in LSTM for OCR
Neural networks have become the technique of choice for OCR, but many aspects
of how and why they deliver superior performance are still unknown. One key
difference between current neural network techniques using LSTMs and the
previous state-of-the-art HMM systems is that HMM systems have a strong
independence assumption. In comparison LSTMs have no explicit constraints on
the amount of context that can be considered during decoding. In this paper we
show that they learn an implicit LM and attempt to characterize the strength of
the LM in terms of equivalent n-gram context. We show that this implicitly
learned language model provides a 2.4\% CER improvement on our synthetic test
set when compared against a test set of random characters (i.e. not naturally
occurring sequences), and that the LSTM learns to use up to 5 characters of
context (which is roughly 88 frames in our configuration). We believe that this
is the first ever attempt at characterizing the strength of the implicit LM in
LSTM based OCR systems
A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations
We introduce an attention-based Bi-LSTM for Chinese implicit discourse
relations and demonstrate that modeling argument pairs as a joint sequence can
outperform word order-agnostic approaches. Our model benefits from a partial
sampling scheme and is conceptually simple, yet achieves state-of-the-art
performance on the Chinese Discourse Treebank. We also visualize its attention
activity to illustrate the model's ability to selectively focus on the relevant
parts of an input sequence.Comment: To appear at ACL2017, code available at
https://github.com/sronnqvist/discourse-ablst
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