Título:
Post-OCR correction using large language models with constrained decoding
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Autor:
Sastre, Ignacio
Etcheverry, Lorena
Rey, Guillermo
Moncecchi, Guillermo
Rosá, Aiala
Etcheverry, Lorena
Rey, Guillermo
Moncecchi, Guillermo
Rosá, Aiala
Tutor:
Tipo de documento:
Preprint
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Palabras clave:
Natural Language Processing
Optical Character Recognition (OCR)
Post-OCR Correction
LLMs with Constrained Decoding
Optical Character Recognition (OCR)
Post-OCR Correction
LLMs with Constrained Decoding
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Año de publicación:
2025
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Resumen:
This article addresses the problem of correcting noisy Optical Character Recognition (OCR) outputs from digitized historical documents, specifically those from the Berrutti Archive related to Uruguay’s civic-military dictatorship. These documents—produced with typewriters, diverse layouts, and overlaid annotations—pose significant hallenges for standard OCR tools, resulting in highly errorprone text. We present a novel post-OCR correction method that leverages fine-tuned open-source Large Language Models (LLMs)
combined with a constrained decoding strategy. This strategy incorporates character-level similarity between the OCR input and the generated output at decoding time, steering the model toward corrections that closely preserve the original text structure. We evaluate our method on a gold-standard dataset of over 2000 annotated lines and show that it outperforms prompting and standard fine-tuning
approaches, reducing both character error rate (CER) and word error rate (WER). The corrected outputs provide more accurate input for downstream tasks, such as named entity recognition, relation and event extraction, and knowledge graph construction, thereby supporting the broader goal of extracting knowledge from historically significant and sensitive archives.
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Proyecto ANII IA_1_2022_1_173863
Citación:
Sastre, I., Etcheverry, L., Rey, G. y otros. Post-OCR correction using large language models with constrained decoding [Preprint] Publicado en: Researche Square, julio 2025. 16 p. DOI: doi.org/10.21203/rs.3.rs-6823036/v1.
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Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| Principal SERMR25.pdf | Preprint | 1.84 MB | Adobe PDF |