Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: OCR Improves Machine Translation for Low-Resource Languages   We aim to investigate the performance of current OCR systems on low resource
languages and low resource scripts. We introduce and make publicly available a
novel benchmark, OCR4MT, consisting of real and synthetic data, enriched with
noise, for 60 low-resource languages in low resource scripts. We evaluate
state-of-the-art OCR systems on our benchmark and analyse most common errors.
We show that OCR monolingual data is a valuable resource that can increase
performance of Machine Translation models, when used in backtranslation. We
then perform an ablation study to investigate how OCR errors impact Machine
Translation performance and determine what is the minimum level of OCR quality
needed for the monolingual data to be useful for Machine Translation.

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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: stat