Abstract: A method for producing, or training, a set of character templates uses as the source of training samples an image source of character images, called glyphs, that are not previously segmented or isolated for training. Also used is a labeled glyph position data structure that includes, for each glyph in the image source, a glyph image position in the image source associating an image location of the glyph with a character label paired with the glyph image position that indicates the character in the character set being trained. The labeled glyph position data is used to identify a collection of glyph sample image regions in the image source for each character in the character set; each glyph sample image region is large enough to contain a glyph and typically contains adjacent glyphs for other characters.

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.