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.

本文介绍了一种用于端到端场景文本识别的训练方法，该方法利用多语言数据集进行编码器的预训练，将编码器上的知识泛化到目标语言；同时，使用目标语言的数据集训练解码器以提高模型在目标语言数据上的表现。实验表明，该方法在小规模数据集上具有较好的识别效果。

利用资源丰富的语言数据集进行资源匮乏语言的端到端场景文本识别

Utilizing Resource-Rich Language Datasets for End-to-End Scene Text  Recognition in Resource-Poor Languages

Sinhala is the native language of the Sinhalese people who make up the
largest ethnic group of Sri Lanka. The language belongs to the globe-spanning
language tree, Indo-European. However, due to poverty in both linguistic and
economic capital, Sinhala, in the perspective of Natural Language Processing
tools and research, remains a resource-poor language which has neither the
economic drive its cousin English has nor the sheer push of the law of numbers
a language such as Chinese has. A number of research groups from Sri Lanka have
noticed this dearth and the resultant dire need for proper tools and research
for Sinhala natural language processing. However, due to various reasons, these
attempts seem to lack coordination and awareness of each other. The objective
of this paper is to fill that gap of a comprehensive literature survey of the
publicly available Sinhala natural language tools and research so that the
researchers working in this field can better utilize contributions of their
peers. As such, we shall be uploading this paper to arXiv and perpetually
update it periodically to reflect the advances made in the field.

本文对公开可用的信杭拉自然语言处理工具和研究进行了全面的文献综述，以帮助该领域的研究人员更好地利用同行的贡献。