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        <title>Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model</title>
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        <namePart type="given">Eva</namePart>
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        <namePart type="given">Cyril</namePart>
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    <name type="personal">
        <namePart type="given">Brigitte</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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        <name type="personal">
            <namePart type="given">Taro</namePart>
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    <abstract>In this paper we present a novel approach to the automatic correction of OCR-induced orthographic errors in a given text. While current systems depend heavily on large training corpora or external information, such as domain-specific lexicons or confidence scores from the OCR process, our system only requires a small amount of (relatively) clean training data from a representative corpus to learn a character-based statistical language model using Bidirectional Long Short-Term Memory Networks (biLSTMs). We demonstrate the versatility and adaptability of our system on different text corpora with varying degrees of textual noise, including a real-life OCR corpus in the medical domain.</abstract>
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%0 Conference Proceedings
%T Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model
%A D’hondt, Eva
%A Grouin, Cyril
%A Grau, Brigitte
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F dhondt-etal-2017-generating
%X In this paper we present a novel approach to the automatic correction of OCR-induced orthographic errors in a given text. While current systems depend heavily on large training corpora or external information, such as domain-specific lexicons or confidence scores from the OCR process, our system only requires a small amount of (relatively) clean training data from a representative corpus to learn a character-based statistical language model using Bidirectional Long Short-Term Memory Networks (biLSTMs). We demonstrate the versatility and adaptability of our system on different text corpora with varying degrees of textual noise, including a real-life OCR corpus in the medical domain.
%U https://aclanthology.org/I17-1101
%P 1006-1014

Markdown (Informal)

[Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model](https://aclanthology.org/I17-1101) (D’hondt et al., IJCNLP 2017)

• Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model (D’hondt et al., IJCNLP 2017)

ACL

• Eva D’hondt, Cyril Grouin, and Brigitte Grau. 2017. Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1006–1014, Taipei, Taiwan. Asian Federation of Natural Language Processing.