Abstract: Systems and methods are disclosed for content aware digital image enhancement. In particular, in one or more embodiments, the disclosed systems and methods analyze content of a digital image portraying a document with graphics and/or text and generate a cleaning confidence map. Specifically, in one or more embodiments, the disclosed systems and methods generate a cleaning confidence map indicating a likelihood that each pixel in the digital image portrays text or a graphic. Moreover, in one or more embodiments, the disclosed systems and methods utilize the cleaning confidence map as a reflection of how aggressively to modify digital images. In particular, in one or more embodiments, the disclosed systems and methods utilize the cleaning confidence map to remove shadows, identify and clean background pixels, and correct contrast in relation to the digital image.

Abstract: Systems and methods are disclosed for content aware digital image enhancement. In particular, in one or more embodiments, the disclosed systems and methods analyze content of a digital image portraying a document with graphics and/or text and generate a cleaning confidence map. Specifically, in one or more embodiments, the disclosed systems and methods generate a cleaning confidence map indicating a likelihood that each pixel in the digital image portrays text or a graphic. Moreover, in one or more embodiments, the disclosed systems and methods utilize the cleaning confidence map as a reflection of how aggressively to modify digital images. In particular, in one or more embodiments, the disclosed systems and methods utilize the cleaning confidence map to remove shadows, identify and clean background pixels, and correct contrast in relation to the digital image.

Abstract: Object boundary detection techniques are described. In implementations, edges of an object displayed in an image are detected. The image is used to generate a gradient image and a monochrome image. Directional filters are applied to the monochrome image to generate directionally filtered images, which are then applied to the gradient image to identify potential boundary lines of the object displayed in the image. A selection can then be made from the potential boundary lines, based on a score associated with each respective potential boundary line, to select lines to represent the boundaries of the object. The selected lines can be used to segment the image into a plurality of segments, and then, using the average color of each segment, one or more of the selected lines can be invalidated as being a false boundary.

Abstract: Object boundary detection techniques are described. In implementations, edges of an object displayed in an image are detected. The image is used to generate a gradient image and a monochrome image. Directional filters are applied to the monochrome image to generate directionally filtered images, which are then applied the gradient image to identify potential boundary lines of the object displayed in the image. A selection can then be made from the potential boundary lines, based on a score associated with each respective potential boundary line, to select lines to represent the boundaries of the object. The selected lines can be used to segment the image into a plurality of segments, and then, using the average color of each segment, one or more of the selected lines can be invalidated as being a false boundary.