Document workflow can be as easy as saving a file to a single location to as complex as decision tree document routing rules. Throw some paper into the mix and the problem intensifies slightly. Getting your paper documents to fit your already accepted digital document workflow can be challenging. Some organizations choose to keep the paper and digital workflows separate. Others unite them but create separate rules for each. For most however, it would be ideal to have a single workflow engine or product supporting both the digital, image, and paper documents.
To do so with the greatest value, you need not only document conversion using Optical Character Recognition ( OCR ), but some other advanced imaging and recognition tools. In the digital document world, you don’t have only the data contained in the document, you have various other meta data items such as file name, file location ( taxonomy ), tags, etc. In order to marry paper with digital the same has to be duplicated on the paper document and has to occur at time of document processing. This could be a manual process or automated, and depending on your paper volume doing it in manual may be OK. To compete with the efficiency of digital documents however, automatic is the way to go.
Using OCR, image-based and contextual-based classification, paper or image documents that enter the workflow can obtain the same value as digital documents. The OCR is responsible for getting all the content from the document. The purpose of this content is for search, indexing, auto-filing, as well as generation of keywords ( tags ) associated with a taxonomy. In order to determine where the document fits into a taxonomy, you must first classify it.
For classification to be most effective, it happens on two levels. Image-based classification, which is what the document looks like, classifies documents based on their physical structure which is a good indicator of its type and very fast. Contextual classification, which is what words are contained in the document, is one level deeper in classification and looks for the keywords that would make a document one type over another. For some environments, image-based classification can do the job entirely. Once classification is known, a classification engine can place the document in the correct spot in an existing taxonomy. Once an ID or classification is determined, it is no challenge to apply tags, file-naming, and file location to a document.
Workflow can stand alone, but injected with the power of OCR and document classification, it becomes a power house that does not know the difference between paper and digital.
Chris Riley – AboutFind much more about document technologies at www.cvisiontech.com.