EMNLP 2020: OCR Post Correction for Endangered Language Texts

EMNLP 2020

EMNLP 2020: OCR Post Correction for Endangered Language Texts

Dec 11, 2020
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Shruti Rijhwani, Antonios Anastasopoulos, Graham Neubig https://arxiv.org/abs/2011.05402 https://twitter.com/shrutirij There is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats that are not machine-readable, such as paper books and scanned images. In this work, we address the task of extracting text from these resources. We create a benchmark dataset of transcriptions for scanned books in three critically endangered languages and present a systematic analysis of how general-purpose OCR tools are not robust to the data-scarce setting of endangered languages. We develop an OCR post-correction method tailored to ease training in this data-scarce setting, reducing the recognition error rate by 34% on average across the three languages. EMNLP 2020 #NLProc Natural Language Processing

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