Abstract: A system encodes a separate assist channel that carries only a small amount of additional information in a hardcopy document to compensate for failure of an OCR system to accurately reconstruct a scanned electronic version of the hardcopy document. The assist channel is encoded by mapping data from a primary channel of the document into a plurality of groups. The plurality of groups is used to classify the symbols of the primary channel by the likelihood that a character will occur and the likelihood that the symbols of the primary channel will be confused during OCR processing.

Abstract: A document display system arranges images of a document ordered in a linear array of pages on a display screen. One page of the document is defined as a focus page which is displayed at the center of the display screen. Images of pages preceding the focus page in the linear array of pages are presented to a user using a first recursive block that is located to the left of the focus page on the display screen. Images of pages following the focus page in the linear array of pages are presented to a user using a second recursive block that is located to the right of the focus page on the display screen. Each recursive block is initially filled with images that are arranged proximate to the focus page in the array of pages. This arrangement of the pages of a document on the display screen provides a context within which to view the selected focus page of a document.

Abstract: A method and system for automatically modifying an original transcription produced as the output of a recognition operation produces a second, modified transcription, such as, for example, automatically correcting an errorful transcription produced by an OCR operation. The invention uses information in an input text image of character images and in an original transcription associated with the input text image to modify aspects of a formal image source model that models as a grammar the spatial image structure of a set of text images. A recognition operation is then performed on the input text image using the modified formal image source model to produce a second, modified transcription. When the original transcription is errorful, the second transcription is a corrected transcription. Several aspects of the formal image source model may be modified; in particular, character templates to be used in the recognition operation are trained in the font of the glyphs occurring in the input text image.

Abstract: A method for establishing a relationship between a text image and a transcription associated with the text image uses conventional image processing techniques to identify one or more geometric attributes, or image parameters, of each of a sequence of regions of the text image. The transcription labels in the transcription are analyzed to determine a comparable set of parameters in transcription label sequence. A matching operation then matches the respective parameters of the two sequences to identify image regions that match with transcription regions. The result is an output data structure that minimally identifies image locations of interest to a subsequent operation that processes the text image. The output data structure may also pair each of the image locations of interest to a transcription location, in effect producing a set of labeled image locations. In one embodiment, the sequence of locations of words and their observed lengths in the text image are determined.

Abstract: A technique for automatically training a set of character templates using unsegmented training samples uses as input a two-dimensional (2D) image of characters, called glyphs, as the source of training samples, a transcription associated with the 2D image as a source of labels for the glyph samples, and an explicit, formal 2D image source model that models as a grammar the structural and functional features of a set of 2D images that may be used as the source of training data. The input transcription may be a literal transcription associated with the 2D input image, or it may be nonliteral, for example containing logical structure tags for document formatting, such as found in markup languages. The technique uses spatial positioning information about the 2D image modeled by the 2D image source model and uses labels in the transcription to determine labeled glyph positions in the 2D image that identify locations of glyph samples.

Abstract: A technique for automatically producing, or training, a set of bitmapped character templates defined according to the sidebearing model of character image positioning uses as input a text line image of unsegmented characters, called glyphs, as the source of training samples. The training process also uses a transcription associated with the text line image, and an explicit, grammar-based text line image source model that describes the structural and functional features of a set of possible text line images that may be used as the source of training samples. The transcription may be a literal transcription of the line image, or it may be nonliteral, for example containing logical structure tags for document formatting and layout, such as found in markup languages.