Summary of Confidence-aware Document Ocr Error Detection, by Arthur Hemmer et al.
Confidence-Aware Document OCR Error Detection
by Arthur Hemmer, Mickaël Coustaty, Nicola Bartolo, Jean-Marc Ogier
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the role of Optical Character Recognition (OCR) confidence scores in enhancing post-OCR error detection. The authors analyze the correlation between confidence scores and error rates across various OCR systems, including commercial and open-source models. They develop ConfBERT, a BERT-based model that incorporates OCR confidence scores into token embeddings, allowing for optional pre-training to adjust for noise. Experimental results show that integrating OCR confidence scores improves error detection capabilities. The study highlights the importance of OCR confidence scores in improving accuracy and reveals significant performance disparities between commercial and open-source OCR technologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure computers can read written text correctly. When computers make mistakes, it’s hard to fix them. To help with this problem, researchers looked at how well different computer programs (called OCR) are able to read text. They found that by using a special score that shows how confident the computer is in its reading, they can improve their mistake-detection abilities. The study also compares how well different commercial and free computer programs do at recognizing written text. |
Keywords
» Artificial intelligence » Bert » Token
