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Unsupervised training of character templates using unsegmented samples

  • US 5,956,419 A
  • Filed: 04/28/1995
  • Issued: 09/21/1999
  • Est. Priority Date: 04/28/1995
  • Status: Expired due to Term
First Claim
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1. A method of operating a machine to perform unsupervised training of a set of character templates, the machine including a processor and a memory device for storing data, the data including instruction data the processor executes to operate the machine, the processor being coupled to the memory device for accessing the data, the method comprising the steps of:

  • A) receiving and storing an image glyph source including a plurality of image pixel positions defining an image, the image glyph source including a plurality of glyphs, each glyph being an image instance of a respective one of a plurality of characters in a character set, the set of character templates being trained representing respective ones of the plurality of characters in the character set;

    B) performing a recognition operation on the image glyph source, the recognition operation producing a plurality of labeled glyph position data items, each labeled glyph position data item indicating one of the plurality of image pixel positions in the image glyph source and a respectively paired glyph label paired with the image pixel position, each image pixel position associating an image glyph source location with a glyph occurring in the image glyph source, each respectively paired glyph label identifying the glyph associated with the image pixel position as a respective one of the plurality of characters in the character set;

    C) determining a sample image region included in the image glyph source for each labeled glyph position data item, the sample image region including the image pixel position indicating the image glyph source location of a glyph and being identified as a training data sample for the character template indicated by the respectively paired glyph label, each sample image region including a plurality of sample pixel positions in the image glyph source each sample pixel position indicating a sample pixel value;

    D) for each respective character template to be trained producing a template image region including a plurality of template pixel positions for storing the respective character template; and

    E) producing the set of character templates using the template image regions and the sample image regions by the sub-steps of;

    (a) producing an image definition data structure for defining and storing an ideal image, the ideal image being represented as a function of the set of character templates being trained, and being a reconstruction of the image glyph source formed by positioning respective ones of the character templates in an image plane at image pixel positions identified as image glyph source locations of glyphs occurring in the image glyph source, each respective one of the character templates positioned in the ideal image being identified by the glyph label paired with the image glyph source location;

    (b) computing pixel scores for template pixel positions in template image regions using selected ones of the sample pixel positions in selected ones of the sample image regions included in the image source of glyphs; and

    (c) sequentially assigning a pixel value to selected template pixel positions in selected template image regions, the selected template pixel positions being selected on the basis of the pixel scores optimizing the function representing the ideal image such that, when all template pixel positions have been assigned pixel values, the pixel value assigned to each selected template pixel position optimizes a matching score measuring a match between the image glyph source and the ideal image.

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