What should you do if document text has poor-quality OCR and needs updating?

When dealing with document text that has poor-quality optical character recognition (OCR) results, rebuilding the index entirely is often the most effective approach. This method allows for the integration of corrected or updated text based on thorough re-evaluation of the original documents. By reconstructing the index, any inconsistencies, inaccuracies, or errors that emerged from the initial OCR process can be resolved systematically, leading to improved searchability and retrieval of information.

Additionally, an entirely rebuilt index ensures that all changes made during the correction process are reflected accurately, making the documents more usable and reliable for future reference. This is particularly important in a project management context, where the accuracy of documentation can affect communication, decision-making, and compliance with standards.

While alternatives such as making manual corrections may seem feasible, they often lead to fragmented data that can create discrepancies and are not as comprehensive as a full index rebuild. Incremental builds might miss out on the broader structural improvements needed after significant errors in the OCR process. Archiving the documents without correcting them would also render them less functional, defeating the purpose of digital documentation in a project management environment.