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        <title>Effective Synthetic Data and Test-Time Adaptation for OCR Correction</title>
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        <namePart type="given">Shuhao</namePart>
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    <name type="personal">
        <namePart type="given">Cheng</namePart>
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    <name type="personal">
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    <name type="personal">
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            <namePart type="given">Yun-Nung</namePart>
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    <abstract>Post-OCR technology is used to correct errors in the text produced by OCR systems. This study introduces a method for constructing post-OCR synthetic data with different noise levels using weak supervision. We define Character Error Rate (CER) thresholds for “effective” and “ineffective” synthetic data, allowing us to create more useful multi-noise level synthetic datasets. Furthermore, we propose Self-Correct-Noise Test-Time Adaptation (SCN-TTA), which combines self-correction and noise generation mechanisms. SCN-TTA allows a model to dynamically adjust to test data without relying on labels, effectively handling proper nouns in long texts and further reducing CER. In our experiments we evaluate a range of models, including multiple PLMs and LLMs. Results indicate that our method yields models that are effective across diverse text types. Notably, the ByT5 model achieves a CER reduction of 68.67% without relying on manually annotated data</abstract>
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%0 Conference Proceedings
%T Effective Synthetic Data and Test-Time Adaptation for OCR Correction
%A Guan, Shuhao
%A Xu, Cheng
%A Lin, Moule
%A Greene, Derek
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F guan-etal-2024-effective
%X Post-OCR technology is used to correct errors in the text produced by OCR systems. This study introduces a method for constructing post-OCR synthetic data with different noise levels using weak supervision. We define Character Error Rate (CER) thresholds for “effective” and “ineffective” synthetic data, allowing us to create more useful multi-noise level synthetic datasets. Furthermore, we propose Self-Correct-Noise Test-Time Adaptation (SCN-TTA), which combines self-correction and noise generation mechanisms. SCN-TTA allows a model to dynamically adjust to test data without relying on labels, effectively handling proper nouns in long texts and further reducing CER. In our experiments we evaluate a range of models, including multiple PLMs and LLMs. Results indicate that our method yields models that are effective across diverse text types. Notably, the ByT5 model achieves a CER reduction of 68.67% without relying on manually annotated data
%U https://aclanthology.org/2024.emnlp-main.862
%P 15412-15425

Markdown (Informal)

[Effective Synthetic Data and Test-Time Adaptation for OCR Correction](https://aclanthology.org/2024.emnlp-main.862) (Guan et al., EMNLP 2024)

• Effective Synthetic Data and Test-Time Adaptation for OCR Correction (Guan et al., EMNLP 2024)

ACL

• Shuhao Guan, Cheng Xu, Moule Lin, and Derek Greene. 2024. Effective Synthetic Data and Test-Time Adaptation for OCR Correction. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15412–15425, Miami, Florida, USA. Association for Computational Linguistics.