A system and method that supports the efficient interactive identification of the most paper intensive document categories such that a maximum number of the documents belonging to those categories can be correctly categorized with a minimum effort and within a minimum amount of time is disclosed. Further, an iterative method combining automatic grouping mechanisms with human labelling. The system and method are configured to allow the automatic machine labelling to run iteratively to generate improved document clustering and categorization.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for interactive labelling of documents associated with one or more printing systems used in an organization, the method comprising: a) receiving a representative set of unlabelled printed documents from the one or more printing systems; b) processing at least one document from the representative set of unlabelled printed documents to generate a plurality of clusters of printed documents, where each cluster contains documents with a subset of similarities; c) processing at least one document from the representative set of unlabelled printed documents to generate a plurality of categories of printed documents, where each category contains documents with a subset of similarities; d) generating a list of clusters and categories for user review; e) receiving document clustering information for one or more documents based on the list of clusters; f) receiving document category validation information from a user for one or more documents based on the list of categories; g) updating a trainer to classify all or part of a set of labelled documents from the machine learning and human labelling phases; h) using the updated trainer, classifying one or more printed documents received in step a) which remain unlabelled; and i) displaying to a user a list of categories and clusters generated by the machine learning and human labelling phases and a list of KPIs generated based upon the representative set of unlabelled documents and the labelled documents. 2. The method of claim 1, wherein generating a plurality of clusters of printed documents further includes grouping remaining unlabelled documents to obtain a limited number of homogenous clusters for review by a user. 3. The method of claim 1, wherein generating a plurality of categories further includes: generating a set of proposed categories based on available sets of already labelled documents; receiving documents proposed by a categorizer to quickly reduce the number of remaining unlabelled documents; reviewing the proposed documents to accept relevant documents and remove irrelevant documents from the proposed categories; and updating a categorizer model using the relevant and irrelevant documents. 4. The method of claim 1, wherein receiving document clustering information includes receiving a homogenous set of documents belonging to a list of available categories, labelled with the corresponding category. 5. The method of claim 4, wherein if the category does not exist, updating the list of available categories to include the category. 6. The method of claim 1, wherein receiving document clustering information further includes selecting an salient document to identify, retrieve and select the group of similar documents for labelling and reorder the documents according to their similarity with this document. 7. The method of claim 4, wherein the document clustering information includes a default visual grouping of similar documents. 8. The method of claim 1, receiving document category validation information from a user further includes reviewing proposed categories for labelled documents and accept or reject the category label for the proposed document. 9. The method of claim 1, wherein displaying provides a KPI to gauge current progress, providing feedback about the actual progress and efficiency of the labelling process and its possible impact in terms of coverage of the overall document collection with sufficient confidence. 10. The method of claim 5, wherein the KPI can further gauge cost and benefit of labelling more documents according to the current cluster and category characteristics and heterogeneity. 11. A system for interactive labelling of documents associated with one or more printing systems used in an organization, the system comprising: a) a receiving a representative set of unlabelled printed documents from the one or more printing systems; b) a clustering component configured to process at least one document from the representative set of unlabelled printed documents to generate a plurality of clusters of printed documents, where each cluster contains documents with a subset of similarities; c) a categorizer component configured to process at least one document from the representative set of unlabelled printed documents to generate a plurality of categories of printed documents, where each category contains documents with a subset of similarities; d) a compiler configured to generate a list of clusters and categories for user review; e) a receiver configured to received document clustering information for one or more documents based on the list of clusters and document category validation information from a user for one or more documents based on the list of categories; f) a training component configured to classify all or part of a set of labelled documents from the machine learning and human labelling phases and using the updated trainer, classifying one or more printed documents received in step a) which remain unlabelled; and g) a display configured to display to a user a list of categories and clusters generated by the machine learning and human labelling phases and a list of KPIs generated based upon the representative set of unlabelled documents and the labelled documents. 12. The system of claim 11, wherein the clustering component is further configured to group remaining unlabelled documents to obtain a limited number of homogenous clusters for review by a user. 13. The system of claim 11, wherein categorizer component is further configured to: generate a set of proposed categories based on available sets of already labelled documents; receive documents proposed by a categorizer to quickly reduce the number of remaining unlabelled documents; review the proposed documents to accept relevant documents and remove irrelevant documents from the proposed categories; and update a categorizer model using the relevant and irrelevant documents. 14. The system of claim 11, wherein the receiver is configured to receive a homogenous set of documents belonging to a list of available categories, labelled with the corresponding category. 15. The system of claim 14, wherein if the category does not exist, updating the list of available categories to include the category. 16. The system of claim 11, wherein the receiver is further configured to receive a selected salient document used to identify, retrieve and select the group of similar documents for labelling and reorders the documents according to their similarity with this document. 17. The system of claim 11 wherein the document clustering information includes a default visual grouping of similar documents. 18. The system of claim 11, receiving document category validation information from a user further includes reviewing proposed categories for labelled documents and accept or reject the category label for the proposed document. 19. The system of claim 11, wherein displaying provides a KPI to gauge current progress, providing feedback about the actual progress and efficiency of the labelling process and its possible impact in terms of coverage of the overall document collection with sufficient confidence. 20. A computer program product comprising a non-transitory recording medium storing instructions which, when executed by a computer processor, perform a method for interactive labelling of documents associated with one or more printing systems used in an organization, the method comprising: a) receiving a representative set of unlabelled printed documents from the one or more printing systems; b) processing at least one document from the representative set of unlabelled printed documents to generate a plurality of clusters of printed documents, where each cluster contains documents with a subset of similarities; c) processing at least one document from the representative set of unlabelled printed documents to generate a plurality of categories of printed documents, where each category contains documents with a subset of similarities; d) generating a list of clusters and categories for user review; e) receiving document clustering information for one or more documents based on the list of clusters; f) receiving document category validation information from a user for one or more documents based on the list of categories; g) updating a trainer to classify all or part of a set of labelled documents from the machine learning and human labelling phases; h) using the updated trainer, classifying one or more printed documents received in step a) which remain unlabelled; and i) displaying to a user a list of categories and clusters generated by the machine learning and human labelling phases and a list of KPIs generated based upon the representative set of unlabelled documents and the labelled documents.