Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Utilizing Resource-Rich Language Datasets for End-to-End Scene Text
  Recognition in Resource-Poor Languages   This paper presents a novel training method for end-to-end scene text
recognition. End-to-end scene text recognition offers high recognition
accuracy, especially when using the encoder-decoder model based on Transformer.
To train a highly accurate end-to-end model, we need to prepare a large
image-to-text paired dataset for the target language. However, it is difficult
to collect this data, especially for resource-poor languages. To overcome this
difficulty, our proposed method utilizes well-prepared large datasets in
resource-rich languages such as English, to train the resource-poor
encoder-decoder model. Our key idea is to build a model in which the encoder
reflects knowledge of multiple languages while the decoder specializes in
knowledge of just the resource-poor language. To this end, the proposed method
pre-trains the encoder by using a multilingual dataset that combines the
resource-poor language's dataset and the resource-rich language's dataset to
learn language-invariant knowledge for scene text recognition. The proposed
method also pre-trains the decoder by using the resource-poor language's
dataset to make the decoder better suited to the resource-poor language.
Experiments on Japanese scene text recognition using a small, publicly
available dataset demonstrate the effectiveness of the proposed method.

28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con