Abstract: A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.

Abstract: A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.

Abstract: A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.

Abstract: A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.

Abstract: A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.

Abstract: A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.

Abstract: A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.

Abstract: A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.

Abstract: Disclosed are embodiments for a system, method, and computer program product for performing an process on an original image, the process being implemented by a computer system performs a comprising the at least one computer: performing an process on an image that renders the processed image legible than then the original image, wherein the analysis segregates dark pixels of the image from light pixels of the image. The method can comprise: first converting the image into a grayscale image. The method comprises processing a pixel area for each pixel of the image is a dark pixel or a light pixel and determining if a pixel is proximate to an edge.

Abstract: A system and method are described for enhancing readability of scanned document images by operating on each document individually. Via principle component analysis, edge detection and local color contrast computation, an automated method removes image background noise and improves sharpness of the scripts and characters.

Abstract: Disclosed are embodiments for a system, method, and computer program product for performing an process on an original image, the process being implemented by a computer system performs a comprising the at least one computer: performing an process on an image that renders the processed image legible than then the original image, wherein the analysis segregates dark pixels of the image from light pixels of the image. The method can comprise: first converting the image into a grayscale image. The method comprises processing a pixel area for each pixel of the image is a dark pixel or a light pixel and determining if a pixel is proximate to an edge.

Abstract: Disclosed are embodiments for a system, method, and computer program product for performing an process on an original image, the process being implemented by a computer system performs a comprising the at least one computer: performing an process on an image that renders the processed image legible than then the original image, wherein the analysis segregates dark pixels of the image from light pixels of the image. The method can comprise: first converting the image into a grayscale image. The method comprises processing a pixel area for each pixel of the image is a dark pixel or a light pixel and determining if a pixel is proximate to an edge.

Abstract: Disclosed are embodiments for a system, method, and computer program product for performing a process on an original image, the process being implemented by a computer system comprising at least one computer: performing a process on at least one image that renders the processed image more legible than then the original image.

Abstract: Disclosed are embodiments for a system, method, and computer program product for performing an process on an original image, the process being implemented by a computer system performs a comprising the at least one computer: performing an process on an image that renders the processed image legible than then the original image, wherein the analysis segregates dark pixels of the image from light pixels of the image. The method can comprise: first converting the image into a grayscale image. The method comprises processing a pixel area for each pixel of the image is a dark pixel or a light pixel and determining if a pixel is proximate to an edge.

Abstract: A method for executing a query comprising a sequence of operations to be performed on one or more relational databases comprises statistically sampling the relational databases at the times the operations are to be executed and then dynamically optimizing the performance of the operations based on the statistical samples obtained as a result of the sampling step.

Abstract: A data processing system comprising an active and intelligent main store including a main memory, a main store controller for accessing the main memory in a manner allowing different address and data structures, and a main store bus connected to the controller. At least one processor of a first type is connected to the main store bus, this being an auxiliary processor for performing input-output and other operations. At least one processor of a second type also is connected to the main store bus, this being an execution processor for fetching, decoding and executing instructions. All or some of either or both of the auxiliary processors and execution processors may be different. A supervisory processor for initiating configuring and monitoring the system is connected to the main store bus. A communication bus is connected to the processors of the first and second types and to the supervisory processor. A diagnostic bus connects the supervisory processor to each of the processors of the first and second types.

Abstract: A data processing system comprising an active and intelligent main store including a main memory, a main store controller for accessing the main memory in a manner allowing different address and data structures, and a main store bus connected to the controller. At least one processor of a first type is connected to the main store bus, this being an auxiliary processor for performing input-output and other operations. At least one processor of a second type also is connected to the main store bus, this being an execution processor for fetching, decoding and executing instructions. All or some of either or both of the auxiliary processors and execution processors may be different. A supervisory processor for initiating configuring and monitoring the system is connected to the main store bus. A communication bus is connected to the processors of the first and second types and to the supervisory processor. A diagnostic bus connects the supervisory processor to each of the processors of the first and second types.

Abstract: A data processing system comprising an active and intelligent main store including a main memory, a main store controller for accessing the main memory in a manner allowing different address and data structures, and a main store bus connected to the controller. At least one processor of a first type is connected to the main store bus, this being an auxiliary processor for performing input-output and other operations. At least one processor of a second type also is connected to the main store bus, this being an execution processor for fetching, decoding and executing instructions. All or some of either or both of the auxiliary processors and execution processors may be different. A supervisory processor for initiating configuring and monitoring the system is connected to the main store bus. A communication bus is connected to the processors of the first and second types and to the supervisory processor. A diagnostic bus connects the supervisory processor to each of the processors of the first and second types.