Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Continuous Offline Handwriting Recognition using Deep Learning Models   Handwritten text recognition is an open problem of great interest in the area
of automatic document image analysis. The transcription of handwritten content
present in digitized documents is significant in analyzing historical archives
or digitizing information from handwritten documents, forms, and
communications. In the last years, great advances have been made in this area
due to applying deep learning techniques to its resolution. This Thesis
addresses the offline continuous handwritten text recognition (HTR) problem,
consisting of developing algorithms and models capable of transcribing the text
present in an image without the need for the text to be segmented into
characters. For this purpose, we have proposed a new recognition model based on
integrating two types of deep learning architectures: convolutional neural
networks (CNN) and sequence-to-sequence (seq2seq) models, respectively. The
convolutional component of the model is oriented to identify relevant features
present in characters, and the seq2seq component builds the transcription of
the text by modeling the sequential nature of the text. For the design of this
new model, an extensive analysis of the capabilities of different convolutional
architectures in the simplified problem of isolated character recognition has
been carried out in order to identify the most suitable ones to be integrated
into the continuous model. Additionally, extensive experimentation of the
proposed model for the continuous problem has been carried out to determine its
robustness to changes in parameterization. The generalization capacity of the
model has also been validated by evaluating it on three handwritten text
databases using different languages: IAM in English, RIMES in French, and
Osborne in Spanish, respectively. The new proposed model provides competitive
results with those obtained with other well-established methodologies.

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The categories of this paper are: eess
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
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The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn