Which of the following actions is acceptable for handling OCRed documents?

Including OCRed documents in the data source and training data source is the most acceptable practice when handling OCRed documents. This approach leverages the text extracted from these documents to enhance data processing and analytical capabilities. The inclusion allows for comprehensive training and validation of models since it utilizes a diverse dataset that reflects various document types and formats.

OCRed documents can contain valuable information that can significantly improve the predictive performance and accuracy of machine learning models. By incorporating these documents into the training data source, you ensure that the model learns from a broader range of examples, thus making it better suited to handle real-world applications. This also supports the model in improving its ability to understand different contexts and layouts that may arise in various documents.

On the other hand, merely excluding OCRed documents or relying on them only in isolated instances would not capitalize on the full potential of the data extracted. Manual handling or limiting the documents only to the data source without training considerations could lead to a loss of insights and lower the overall effectiveness of analytical outcomes. It's crucial to integrate OCRed documents thoughtfully, ensuring they contribute fully to both data and training sets.