Abstract: A computer system, product, and method are provided to utilize machine learning to facilitate document processing. A document collection is introduced to an artificial neural network (ANN), which subjects the document collection to table region identification within discretized contiguous areas. The documents are assigned to one or more clusters responsive to the leveraged ANN. Documents are selectively evaluated from the clusters, and one or more label corrections are applied to the ANN. The ANN generates an updated document collection incorporating the applied one or more label corrections.

Abstract: Organizing and/or aligning fragments of text that are included in a set of physical and/or digital documents so that the arrangement of the text fragments is in a readily understandable and meaningful format for a given reader. This organization and/or alignment uses a relation model of the various text fragments to correlate a meaning between and amongst the various text fragments to ultimately determine the final alignment and/or arrangement of those text fragments.

Abstract: One embodiment provides a method, including: receiving two documents, one of the two documents having at least one table that includes the same information as a corresponding table in the other of the two documents, wherein (i) one of the two documents comprises the at least one table in an unstructured table representation and (ii) the other of the two documents comprises the at least one table in a structured table representation; identifying text elements within the at least one table in the unstructured table representation; matching the identified text elements with table elements within the at least one table in the structured table representation; and annotating the at least one table in the structured table representation based upon the matches between the table elements and text elements.

Abstract: Organizing and/or aligning fragments of text that are included in a set of physical and/or digital documents so that the arrangement of the text fragments is in a readily understandable and meaningful format for a given reader. This organization and/or alignment uses a relation model of the various text fragments to correlate a meaning between and amongst the various text fragments to ultimately determine the final alignment and/or arrangement of those text fragments.

Abstract: A computer system, product, and method are provided to utilize machine learning to facilitate document processing. A document collection is introduced to an artificial neural network (ANN), which subjects the document collection to table region identification within discretized contiguous areas. The documents are assigned to one or more clusters responsive to the leveraged ANN. Documents are selectively evaluated from the clusters, and one or more label corrections are applied to the ANN. The ANN generates an updated document collection incorporating the applied one or more label corrections.

Abstract: Methods, systems, and computer program products for table recognition in PDF documents are provided herein. A computer-implemented method includes discretizing one or more contiguous areas of a PDF document; identifying one or more white-space separator lines within the one or more discretized contiguous areas of the PDF document; detecting one or more candidate table regions within the one or more discretized contiguous areas of the PDF document by clustering the one or more white-space separator lines into one or more grids; and outputting at least one of the candidate table regions as a finalized table in accordance with scores assigned to each of the one or more candidate table regions based on (i) border information and (ii) cell structure information.

Abstract: One embodiment provides a method, including: receiving two documents, one of the two documents having at least one table that includes the same information as a corresponding table in the other of the two documents, wherein (i) one of the two documents comprises the at least one table in an unstructured table representation and (ii) the other of the two documents comprises the at least one table in a structured table representation; identifying text elements within the at least one table in the unstructured table representation; matching the identified text elements with table elements within the at least one table in the structured table representation; and annotating the at least one table in the structured table representation based upon the matches between the table elements and text elements.

Abstract: Methods, systems, and computer program products for table recognition in PDF documents are provided herein. A computer-implemented method includes discretizing one or more contiguous areas of a PDF document; identifying one or more white-space separator lines within the one or more discretized contiguous areas of the PDF document; detecting one or more candidate table regions within the one or more discretized contiguous areas of the PDF document by clustering the one or more white-space separator lines into one or more grids; and outputting at least one of the candidate table regions as a finalized table in accordance with scores assigned to each of the one or more candidate table regions based on (i) border information and (ii) cell structure information.

Abstract: Systems and methods for processing Machine Learning (ML) algorithms in a MapReduce environment are described. In one embodiment of a method, the method includes receiving a ML algorithm to be executed in the MapReduce environment. The method further includes parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops). The method also includes automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops). The method further includes implementing the execution plans in the sequence of the plurality of the statement blocks.

Abstract: Systems and methods for processing Machine Learning (ML) algorithms in a MapReduce environment are described. In one embodiment of a method, the method includes receiving a ML algorithm to be executed in the MapReduce environment. The method further includes parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops). The method also includes automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops). The method further includes implementing the execution plans in the sequence of the plurality of the statement blocks.