Abstract: A method for identifying words in a textual image undergoing optical character recognition includes receiving a bitmap of an input image which includes textual lines that have been segmented by a plurality of chop lines. The chop lines are each associated with a confidence level reflecting a degree to which the respective chop line properly segments the textual line into individual characters. One or more words are identified in one of the textual lines based at least in part on the textual lines and a first subset of the plurality of chop lines which have a chop line confidence level above a first threshold value. If the first word is not associated with a sufficiently high word confidence level, at least a second word in the textual line is identified based at least in part on a second subset of the plurality of chop lines which have a confidence level above a second threshold value lower than the first threshold value.

Abstract: Embodiments of camera-based scanning are described. In various embodiments, scanned documents can be created using images captured by a camera associated with a device. An image captured by the camera is processed to identify portions within the image that correspond to rectangular objects such as paper, business cards, whiteboards, screens, and so forth. One or more of these portions can be selected for scanning automatically based on a scoring scheme and/or semi-automatically with the aid of input from a user. One or more scanned documents are created from the selected portions by un-warping the selected portions to remove effects of perspective (e.g., rectify the portions to rectangles) and applying various image enhancements to improve appearance.

Abstract: An optical character recognition process characterizes text lines in a textual image by their base-line, mean-line and x-height. The base-line for at least one text line in the image is determined by finding a parametric curve that maximizes a first fitness function that depends on the values of pixels through which the parametric curve passes and pixels below the parametric curve. The base-line corresponds to the parametric curve for which the first fitness function is maximized. The first fitness function is designed so that it increases with increasing lightless or brightness of pixels immediately below the parametric curve while also increasing with decreasing lightness of pixels through which the parametric curve passes. The mean-line is determined by incrementally shifting the base-line upward by predetermined amounts (e.g., a single pixel) until a second fitness function for the shifted base-line is maximized. The second fitness function is essentially the inverse of the first fitness function.

Abstract: An image deskew system and techniques are used in the context of optical character recognition. An image is obtained of an original set of characters in an original linear (horizontal) orientation. An acquired set of characters, which is skewed relative to the original linear orientation by a rotation angle, is represented by pixels of the image. The rotation angle is estimated, and a confidence value may be associated with the estimation, to determine whether to deskew the image. In connection with rotation angle estimation, an edge detection filter is applied to the acquired set of characters to produce an edge map, which is input to a linear hough transform filter to produce a set of output lines in parametric form. The output lines are assigned scores, and based on the scores, at least one output line is determined to be a dominant line with a slope approximating the rotation angle.

Abstract: An image deskew system and techniques are used in the context of optical character recognition. An image is obtained of an original set of characters in an original linear (horizontal) orientation. An acquired set of characters, which is skewed relative to the original linear orientation by a rotation angle, is represented by pixels of the image. The rotation angle is estimated, and a confidence value may be associated with the estimation, to determine whether to deskew the image. In connection with rotation angle estimation, an edge detection filter is applied to the acquired set of characters to produce an edge map, which is input to a linear hough transform filter to produce a set of output lines in parametric form. The output lines are assigned scores, and based on the scores, at least one output line is determined to be a dominant line with a slope approximating the rotation angle.

Abstract: An image processing apparatus is provided that includes a character chopper component that segments words into individual characters in a bitmap of a textual image undergoing an OCR process. The Character chopper component is configured to produce a set of (possibly curved) chop-lines which divide a bitmap of any given word into its individual character or glyph candidates. Cases where an input bitmap contains two separate words are handled by marking a place where those words should be split. The character segmentation algorithm computes the set of vertically oriented, curved chop-lines by considering glyph and background colors in a given word bitmap. The set is filtered afterwards using various heuristics, in order to preserve those lines that indeed do separate a word's glyphs and minimize the number of those that do not.

Abstract: A method for identifying words in a textual image undergoing optical character recognition includes receiving a bitmap of an input image which includes textual lines that have been segmented by a plurality of chop lines. The chop lines are each associated with a confidence level reflecting a degree to which the respective chop line properly segments the textual line into individual characters. One or more words are identified in one of the textual lines based at least in part on the textual lines and a first subset of the plurality of chop lines which have a chop line confidence level above a first threshold value. If the first word is not associated with a sufficiently high word confidence level, at least a second word in the textual line is identified based at least in part on a second subset of the plurality of chop lines which have a confidence level above a second threshold value lower than the first threshold value.

Abstract: A method for enhancing a textual image for undergoing optical character recognition begins by receiving an image that includes native lines of text. A background line profile is determined which represents an average background intensity along the native lines in the image. Likewise, a foreground line profile is determined which represents an average foreground background intensity along the native lines in the image. The pixels in the image are assigned to either a background or foreground portion of the image based at least in part on the background line profile and the foreground line profile. The intensity of the pixels designated to the background portion of the image is adjusted to a maximum brightness so as to represent a portion of the image that does not include text.

Abstract: An optical character recognition process characterizes text lines in a textual image by their base-line, mean-line and x-height. The base-line for at least one text line in the image is determined by finding a parametric curve that maximizes a first fitness function that depends on the values of pixels through which the parametric curve passes and pixels below the parametric curve. The base-line corresponds to the parametric curve for which the first fitness function is maximized. The first fitness function is designed so that it increases with increasing lightless or brightness of pixels immediately below the parametric curve while also increasing with decreasing lightness of pixels through which the parametric curve passes. The mean-line is determined by incrementally shifting the base-line upward by predetermined amounts (e.g., a single pixel) until a second fitness function for the shifted base-line is maximized. The second fitness function is essentially the inverse of the first fitness function.

Abstract: Embodiments of camera-based scanning are described. In various embodiments, scanned documents can be created using images captured by a camera associated with a device. An image captured by the camera is processed to identify portions within the image that correspond to rectangular objects such as paper, business cards, whiteboards, screens, and so forth. One or more of these portions can be selected for scanning automatically based on a scoring scheme and/or semi-automatically with the aid of input from a user. One or more scanned documents are created from the selected portions by un-warping the selected portions to remove effects of perspective (e.g., rectify the portions to rectangles) and applying various image enhancements to improve appearance.