Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boosting Handwriting Text Recognition in Small Databases with Transfer
  Learning   In this paper we deal with the offline handwriting text recognition (HTR)
problem with reduced training datasets. Recent HTR solutions based on
artificial neural networks exhibit remarkable solutions in referenced
databases. These deep learning neural networks are composed of both
convolutional (CNN) and long short-term memory recurrent units (LSTM). In
addition, connectionist temporal classification (CTC) is the key to avoid
segmentation at character level, greatly facilitating the labeling task. One of
the main drawbacks of the CNNLSTM-CTC (CLC) solutions is that they need a
considerable part of the text to be transcribed for every type of calligraphy,
typically in the order of a few thousands of lines. Furthermore, in some
scenarios the text to transcribe is not that long, e.g. in the Washington
database. The CLC typically overfits for this reduced number of training
samples. Our proposal is based on the transfer learning (TL) from the
parameters learned with a bigger database. We first investigate, for a reduced
and fixed number of training samples, 350 lines, how the learning from a large
database, the IAM, can be transferred to the learning of the CLC of a reduced
database, Washington. We focus on which layers of the network could be not
re-trained. We conclude that the best solution is to re-train the whole CLC
parameters initialized to the values obtained after the training of the CLC
from the larger database. We also investigate results when the training size is
further reduced. The differences in the CER are more remarkable when training
with just 350 lines, a CER of 3.3% is achieved with TL while we have a CER of
18.2% when training from scratch. As a byproduct, the learning times are quite
reduced. Similar good results are obtained from the Parzival database when
trained with this reduced number of lines and this new approach.

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The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
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The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph