1,084 research outputs found
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.Comment: ICFHR 2018 Conferenc
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
Deep LSTM is an ideal candidate for text recognition. However text
recognition involves some initial image processing steps like segmentation of
lines and words which can induce error to the recognition system. Without
segmentation, learning very long range context is difficult and becomes
computationally intractable. Therefore, alternative soft decisions are needed
at the pre-processing level. This paper proposes a hybrid text recognizer using
a deep recurrent neural network with multiple layers of abstraction and long
range context along with a language model to verify the performance of the deep
neural network. In this paper we construct a multi-hypotheses tree architecture
with candidate segments of line sequences from different segmentation
algorithms at its different branches. The deep neural network is trained on
perfectly segmented data and tests each of the candidate segments, generating
unicode sequences. In the verification step, these unicode sequences are
validated using a sub-string match with the language model and best first
search is used to find the best possible combination of alternative hypothesis
from the tree structure. Thus the verification framework using language models
eliminates wrong segmentation outputs and filters recognition errors
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