Philip S Chou, 8768 Ash St, Englewood, NJ 07632

6687404, Feb 3, 2004

Jun 20, 1997

08/880137

Jesse Hull - Boston MA
Philip A. Chou - Bellevue WA
Gary E. Kopec - Belmont CA
Dennis S. Arnon - San Francisco CA

Xerox Corporation - Stamford CT

G06K 900

382226, 382180

A two-dimensional (2D) image model models the layout structure of a class of document images as an image grammar and includes production rules having explicit layout parameters as data items that indicate information about the spatial relationships among image constituents occurring in images included in the class. The parameters are explicitly represented in the grammar rules in a manner that permits them to be automatically trained by a training operation that makes use of sample document images from the class of modeled documents. After each sample image is aligned with the 2D grammar, document-specific measurements about the spatial relationships between image constituents are taken from the image. Optimal values for the layout parameters are then computed from the measurement data collected from all samples. An illustrated implementation of the 2D image model takes the form of a stochastic context-free attribute grammar in which synthesized and inherited attributes and synthesis and inheritance functions are associated with each production rule in the grammar.

5020112, May 28, 1991

Oct 31, 1989

7/429923

Philip A. Chou - Chatham NJ

At&T Bell Laboratories - Murray Hill NJ

G06K 962

382 37

A method of automatically identifying bitmapped image objects. Each of a set of templates in an object template library is compared with all areas of like size of a bitmapped image. A set of signals is generated for each such comparison that satisfies a defined matching criteria between the template and the image area being compared. The set of signals identifies the object based on the matching template, the location of the object in the image and an indication of the goodness of the match between the object and the template. A series of possible parse trees are formed that describe the image with a probability of occurrence for each tree. Each parent node and its child nodes of each parse tree satisfies a grammatical production rule in which some of the production rules define spatial relationships between objects in the image. The one of the possible parse trees which has the largest probability of occurence is selected for further utilization.