Abstract: A method includes one or more program obtaining a natural language query, where the natural language query is comprised of a first group of terms, converting the natural language query to a machine language query, and executing machine language query on at least one computer resource. The program obtains search results responsive to the machine language query, where the search results include related terms derived from terms in the first group of terms utilizing concept expansion. The program parses the search results by applying a statistical information extraction to the terms in the first group and to the related terms to identify entities and generates at least one additional natural language query by incorporating a portion of the identified entities into the query. The identified entities in the new query are a second group of terms. At least one term in the first group is not in the second group.

Abstract: To compress a document, a number of edges present in a selected portion of the document are counted to determine whether the number of edges exceeds a threshold. When the number of edges exceeds the threshold, a pixel is selected from the portion and a set of neighboring pixels is identified for the pixel. For each neighboring pixel in a subset of the neighboring pixels, a corresponding label of the neighboring pixel is identified. A mask layer contains labels of pixels in the portion where a label of the selected pixel is biased using labels of neighboring pixels in the subset of the neighboring pixels. The selected pixel is designated to a foreground or a background layer of the document according to the label of the selected pixel. A compressed document is constructed corresponding to the document using the mask layer, the foreground layer, and the background layer.

Abstract: To compress a document, a number of edges present in a selected portion of the document are counted to determine whether the number of edges exceeds a threshold. When the number of edges exceeds the threshold, a pixel is selected from the portion and a set of neighboring pixels is identified for the pixel. For each neighboring pixel in a subset of the neighboring pixels, a corresponding label of the neighboring pixel is identified. A mask layer contains labels of pixels in the portion where a label of the selected pixel is biased using labels of neighboring pixels in the subset of the neighboring pixels. The selected pixel is designated to a foreground or a background layer of the document according to the label of the selected pixel. A compressed document is constructed corresponding to the document using the mask layer, the foreground layer, and the background layer.

Abstract: Natural language processing is provided. A computer processor, selects a pipeline based on an artifact that includes unstructured data, the pipeline identifying a first algorithm of a first set of algorithms of a first human language technology (HLT) component and a second algorithm of a second set of algorithms of a second HLT component; applies the first algorithm based on the artifact to generate a first cluster space associated with the artifact; amends an evidence chain associated with the artifact in response to applying the first algorithm, wherein the evidence chain includes one or more probabilistic findings of truth corresponding to the artifact; standardizes a first ontology of the first cluster space; applies the second algorithm based on the artifact to generate a second cluster space that is associated with the artifact; and identifies a set of information of one or more corpora that is relevant to the artifact.

Abstract: A system can include a system processor that is programmed or adapted to classify pixels in a document image, and in some instances, to enhance the document image based upon such classification. Image data including pixels is received. For each pixel in some subset of the image data, a feature vector is generated. Each pixel in the subset is classified as text or image based upon its associated feature vector. The classifications can be outputted for storage and/or further processing. Further processing can include pixel level enhancement processing. Such enhancement processing can include application of a sharpening filter to pixels classified as text and of a smoothing filter to pixels classified as image. In some instances, background suppression can be performed upon a subset of the image data prior to classification.

Abstract: A method of performing tonal transform on image data. The method includes extracting a portion of the image data, assigning one of a plurality of labels to the extracted portion, performing a first tonal transformation on the extracted portion if a first label is assigned to the extracted portion, and performing a second tonal transformation on the extracted portion if a second label is assigned to the extracted portion. The method can be performed, for example, with an image processor comprising a memory that stores the image data and a processor coupled to the memory.

Abstract: A scanning or copying system can include imaging elements and one or more system processors that are programmed or adapted to perform image processing methods and algorithms on image data, and in some instances, to enhance the image. Image data is acquired using imaging elements. Some imaging elements may have overlapping or rotated fields of view or employ differing resolutions. For each imaging element, its output is recombined together with the output of one or more other imaging elements. To perform the recombination, the system can extract features in an overlapping region and match these features in multiple images. In some instances, the features matched can be edges. Alternatively, the recombination can be performed by positioning each subimage with respect to a larger image through image matching and location techniques. Parameters from the recombined image can be extracted and these parameters can be used to correct for geometrical and spatial distortions and thereby enhance the image.

Abstract: The invention provides a system and method for JPEG encoding an image that includes variable quantization for each block, depending upon the classification of each block, which is dependent on the classification of pixels in that block.

Abstract: A system can include a system processor that is programmed or adapted to classify pixels in a document image, and in some instances, to enhance the document image based upon such classification. Image data including pixels is received. For each pixel in some subset of the image data, a feature vector is generated. Each pixel in the subset is classified as text or image based upon its associated feature vector. The classifications can be outputted for storage and/or further processing. Further processing can include pixel level enhancement processing. Such enhancement processing can include application of a sharpening filter to pixels classified as text and of a smoothing filter to pixels classified as image. In some instances, background suppression can be performed upon a subset of the image data prior to classification.

Abstract: A scanning or copying system can include imaging elements and one or more system processors that are programmed or adapted to perform image processing methods and algorithms on image data, and in some instances, to enhance the image. Image data is acquired using imaging elements. Some imaging elements may have overlapping or rotated fields of view or employ differing resolutions. For each imaging element, its output is recombined together with the output of one or more other imaging elements. To perform the recombination, the system can extract features in an overlapping region and match these features in multiple images. In some instances, the features matched can be edges. Alternatively, the recombination can be performed by positioning each subimage with respect to a larger image through image matching and location techniques. Parameters from the recombined image can be extracted and these parameters can be used to correct for geometrical and spatial distortions and thereby enhance the image.

Abstract: A system can include a system processor that is programmed or adapted to classify pixels in a document image, and in some instances, to enhance the document image based upon such classification. Image data including pixels is received. For each pixel in some subset of the image data, a feature vector is generated. Each pixel in the subset is classified as text or image based upon its associated feature vector. The classifications can be outputted for storage and/or further processing. Further processing can include pixel level enhancement processing. Such enhancement processing can include application of a sharpening filter to pixels classified as text and of a smoothing filter to pixels classified as image. In some instances, background suppression can be performed upon a subset of the image data prior to classification.