Confidence-Aware Document OCR Error Detection
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917770254352384 |
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| author | Hemmer, Arthur Coustaty, Mickaël Bartolo, Nicola Ogier, Jean-Marc |
| author_facet | Hemmer, Arthur Coustaty, Mickaël Bartolo, Nicola Ogier, Jean-Marc |
| contents | Optical Character Recognition (OCR) continues to face accuracy challenges that impact subsequent applications. To address these errors, we explore the utility of OCR confidence scores for enhancing post-OCR error detection. Our study involves analyzing the correlation between confidence scores and error rates across different OCR systems. We develop ConfBERT, a BERT-based model that incorporates OCR confidence scores into token embeddings and offers an optional pre-training phase for noise adjustment. Our experimental results demonstrate that integrating OCR confidence scores can enhance error detection capabilities. This work underscores the importance of OCR confidence scores in improving detection accuracy and reveals substantial disparities in performance between commercial and open-source OCR technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04117 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Confidence-Aware Document OCR Error Detection Hemmer, Arthur Coustaty, Mickaël Bartolo, Nicola Ogier, Jean-Marc Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Optical Character Recognition (OCR) continues to face accuracy challenges that impact subsequent applications. To address these errors, we explore the utility of OCR confidence scores for enhancing post-OCR error detection. Our study involves analyzing the correlation between confidence scores and error rates across different OCR systems. We develop ConfBERT, a BERT-based model that incorporates OCR confidence scores into token embeddings and offers an optional pre-training phase for noise adjustment. Our experimental results demonstrate that integrating OCR confidence scores can enhance error detection capabilities. This work underscores the importance of OCR confidence scores in improving detection accuracy and reveals substantial disparities in performance between commercial and open-source OCR technologies. |
| title | Confidence-Aware Document OCR Error Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2409.04117 |