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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.14557 |
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| _version_ | 1866908496284352512 |
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| author | Belzarena, Diego Mowlavi, Seginus Artola, Aitor Mariño, Camilo Gardella, Marina Ramírez, Ignacio Tadros, Antoine He, Roy Bottaioli, Natalia Rajaei, Boshra Randall, Gregory Morel, Jean-Michel |
| author_facet | Belzarena, Diego Mowlavi, Seginus Artola, Aitor Mariño, Camilo Gardella, Marina Ramírez, Ignacio Tadros, Antoine He, Roy Bottaioli, Natalia Rajaei, Boshra Randall, Gregory Morel, Jean-Michel |
| contents | Current OCR systems are based on deep learning models trained on large amounts of data. Although they have shown some ability to generalize to unseen data, especially in detection tasks, they can struggle with recognizing low-quality data. This is particularly evident for printed documents, where intra-domain data variability is typically low, but inter-domain data variability is high. In that context, current OCR methods do not fully exploit each document's redundancy. We propose an unsupervised method by leveraging the redundancy of character shapes within a document to correct imperfect outputs of a given OCR system and suggest better clustering. To this aim, we introduce an extended Gaussian Mixture Model (GMM) by alternating an Expectation-Maximization (EM) algorithm with an intra-cluster realignment process and normality statistical testing. We demonstrate improvements in documents with various levels of degradation, including recovered Uruguayan military archives and 17th to mid-20th century European newspapers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14557 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving OCR using internal document redundancy Belzarena, Diego Mowlavi, Seginus Artola, Aitor Mariño, Camilo Gardella, Marina Ramírez, Ignacio Tadros, Antoine He, Roy Bottaioli, Natalia Rajaei, Boshra Randall, Gregory Morel, Jean-Michel Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Current OCR systems are based on deep learning models trained on large amounts of data. Although they have shown some ability to generalize to unseen data, especially in detection tasks, they can struggle with recognizing low-quality data. This is particularly evident for printed documents, where intra-domain data variability is typically low, but inter-domain data variability is high. In that context, current OCR methods do not fully exploit each document's redundancy. We propose an unsupervised method by leveraging the redundancy of character shapes within a document to correct imperfect outputs of a given OCR system and suggest better clustering. To this aim, we introduce an extended Gaussian Mixture Model (GMM) by alternating an Expectation-Maximization (EM) algorithm with an intra-cluster realignment process and normality statistical testing. We demonstrate improvements in documents with various levels of degradation, including recovered Uruguayan military archives and 17th to mid-20th century European newspapers. |
| title | Improving OCR using internal document redundancy |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2508.14557 |