Approaches to Indexing Scanned Documents

When your company has a great deal of paper that it needs to convert into electronic form, one problem that often rears its head is how to properly index the documents so they end up in the right place (network folder, repository location, etc.).  Luckily, there are many different options available to indexing scanned documents:

1. Manual Indexing – with this solution, scanned documents are routed to a data entry professional who manually indexes the document before it is saved to the repository.  Pros: highly accurate, relatively inexpensive if the labor rate is right, Cons: The slowest method, and if you are scanning millions of documents, very impractical.

2. OCR Indexing – with OCR indexing, the text of each document is recognized by a piece of software that looks for certain terms in the text of the document, and places the document in the corresponding storage location.  For instance, if the document includes the words “Scopes Monkey Trial”, then the document should be put in the “Legal” document folder.  Another option with this is to simple place all documents in one “location”, but have them searchable based on their text.  It really depends on what the needs of the business are.  Pros: very fast, fully automated, Cons: not highly accurate, unrecognizable documents may still need to be manually indexed.

3. Bar Code Indexing – with this approach, as each document is scanned, the scanning user selects the proper location for the file (patient record, legal case, etc.) from an application, and the application prints out a barcode sticker that is then applied to the document.  This bar code is then recognized by the scanning software, and the document is routed to the proper location for storage.  Pros: Highly Accurate, quick, Cons: Slightly more manual process on the scanning side.

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As you can see, all of these approaches have their plusses and minuses.  If you need help deciding on the solution that would best for you, please feel free to contact us using the form to the right of this page.

(Image credit: Niklas Bildhauer)

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