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Method for retrieval of arabic historical manuscripts

  • US 9,075,846 B2
  • Filed: 12/12/2012
  • Issued: 07/07/2015
  • Est. Priority Date: 12/12/2012
  • Status: Expired due to Fees
First Claim
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1. A computer-implemented method for retrieval of Arabic historical manuscripts, comprising the steps of:

  • entering Arabic historical manuscript images into a computer for processing;

    extracting circular polar grid features from the Arabic historical manuscript images stored in the computer, wherein the step of extracting circular polar grid features comprises;

    building a circular polar grid from a multiline-axis including an intersection of a 0°

    line, a 45°

    line, a 90°

    line and a 135°

    line;

    overlaying concentric circles centered about the intersection point of said multiline-axis, the concentric circles having radial values of r, 2r, 3r, . . . nr; and

    centering said circular polar grid at a centroid of an image term to be indexed;

    constructing a Latent Semantic Index based on the extracted circular polar grid features, the Latent Semantic Index having a reduced dimension m×

    n Term-by-Document matrix obtained from a Singular Value Decomposition of a higher dimensional Term-by-Document matrix constructed by the computer from the extracted circular polar grid features, wherein m rows represent the features and n columns represent the images;

    accepting a user query against the stored Arabic historical manuscript images, the computer forming the user query as a query vector derived from features extraction of a query image supplied by the user;

    performing query matching based on comparison between the query vector and the Term-by-Document matrix;

    weighing each term of said Term-by-Document matrix by a value representing an occurrence frequency of a feature of said term in said document, wherein the step of weighing each term of said Term-by-Document matrix comprises;

    picking a comprehensive training set of said document for each said feature;

    calculating a mean μ

    f and a standard deviation σ

    f of the features f'"'"'s value across the training set; and

    for each image in the collection, defining an occurrence count Ofj of feature f according to the relation;

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