What document handling approach is recommended for poor-quality OCR documents?

When dealing with poor-quality OCR (Optical Character Recognition) documents, handling them manually is often considered the most effective approach. This is because such documents may contain errors or inaccuracies that can significantly impact data integrity and analysis outcomes. Manual handling allows trained professionals to review the documents, correct mistakes, and ensure that the data extracted meets the necessary quality standards.

Incorporating poor-quality documents into automated processes without review can lead to compounding errors, misinterpretation of data, and unreliable outcomes in analytics. Manual handling enables a more thorough assessment of these documents and the opportunity to apply context or judgment in addressing the issues that arise from OCR errors.

The other approaches, while they may have some merit, do not provide the same level of care or quality assurance. Simply including poor-quality documents in the training data or excluding them entirely limits the potential to improve analytics outcomes. Additionally, including them without manual intervention can exacerbate the problems caused by their inherent quality issues.