Automatic Processing, Classification and Retrieval of Unconstrained Digital Documents

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The amount of paper documents that must be processed by humans in many organizations both commercial and government offices for archival purposes is huge and growing every day. There is an urgent need for automation of this process. For clean printed documents, the problem could be considered as already solved at least in theory. However, the difficulty is in dealing with unconstrained handwritten documents. To the best of our knowledge, to date there is no system capable of classifying document images based on arbitrary text keywords. Recently there has been some success in mail sorting. However in mail sorting the postal codes are written in certain format. The detection of keywords in unconstrained digital documents is a challenging problem in document classification and retrieval applications, and thus has attracted considerable interest by academia and industry in recent years. Keyword detection fulfills two purposes in unconstrained documents. It determines by means of a computer program whether or not a scanned document image contains a text keyword and optionally, spots the instances of the keyword in the document image. The goal of our project is to develop efficient techniques for processing and classification of unconstrained digital documents. We wish to develop a general keyword detection system which will allow the user to classify input document images and to retrieve the documents which contain some necessary information.