What acceptable actions can you take to properly handle OCR'd documents?

The correct action in this scenario involves a deeper understanding of how to utilize OCR'd (Optical Character Recognition) documents effectively within a data processing workflow. While excluding OCR'd documents and handling them manually sounds like a viable option, it is often more efficient and beneficial to include these documents in a data source or training data source, which allows for automation and integration with other data processing tasks.

When documents are OCR'd, they are converted from images or scanned documents into machine-readable text. This conversion allows the information contained within those documents to be utilized in analyses or within a broader data processing context. Including OCR'd documents in the data source ensures that all available information is accessible for data analysis, machine learning models, or further document review processes.

Additionally, integrating OCR'd documents into both the data source and the training data source facilitates enhanced model training. This inclusion allows models to learn from a diverse range of document types and formats, ultimately improving the accuracy and reliability of automated systems when processing similar documents in the future.

Therefore, the most effective approach is to incorporate the OCR'd documents into both the data and training data sources, leveraging their converted content to enhance the overall quality and depth of the data analysis or training being conducted. This process optimizes efficiency and accuracy