BR has a "negation" process in which it can remove or delete certain content, and, depending on what the tables looked like, negation could be used to remove the tables. BR can also be used to redact specific terms. To use your terminology, BR can "automagically" cluster millions of documents based on their visual appearance. The visual classification may provide sufficient differentiation, or you may want to use visual coding to base differentiation on different coded values. For example, visual classification would put  contracts of a certain type in a visual classification. To identify contracts with a specific customer or from a specific zip code, visual coding could be used to create fielded data for customer name or customer zip code - such fields can then be used to differentiate within the classification. ... View more

BeyondRecognition provides a number of document processing technologies for far more than just creating text from images. One of the key functionalities is classifying documents based on visual similarity, NOT based on a textual comparison. BR's visual similarity approach serves to normalize documents regardless of the type of container file, e.g. Word docs, PDF's printed directly from those Word docs, or scanned TIF or image-only PDF copies made from paper printouts of those files, all get classified together despite differences in resolution or orientation.. Well logs, maps, and graphs can be classified based on their appearance. The classification occurs automatically and is scalable to large collections or business processes. BR's visual coding can be used to quickly and accurately extract data elements from the classes for use in subsequent downstream data analytics programs. For more information on BR's text creation, visual classification, visual coding, and logical document boundary determination capabilities, see the BR blog and website at: http://beyondrecognition.net/resources/document-u-blog/ ... View more