Ahmad A Abdulkader

US Patent:

7010165, Mar 7, 2006

Filed:

May 10, 2002

Appl. No.:

10/144233

Inventors:

Gregory Hullender - Bellevue WA, US
Ahmad Abdulkader - Sammamish WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06K 9/18

US Classification:

382186

Abstract:

An electronic ink object is preprocessed for improving handwriting recognition results. An electronic ink object, possibly having multiple lines of non-horizontal ink, is preprocessed by segmenting or separating the electronic ink object into one or more lines, computing a writing angle of each line, and rotating each line so that it is horizontal.

US Patent:

7302099, Nov 27, 2007

Filed:

Nov 10, 2003

Appl. No.:

10/704785

Inventors:

Qi Zhang - Redmond WA, US
Henry A. Rowley - New Castle WA, US
Ahmad A. Abdulkader - Sammamish WA, US
Angshuman Guha - Woodinville WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06K 9/18
G06K 9/00

US Classification:

382186, 382185, 382187

Abstract:

Ink strokes of cursive writing are segmented to make the cursive writing more like print writing, particularly with respect to the number of strokes of a character. A stroke-segmentation module first finds the local extrema points on a stroke of input ink. Then the local extrema points are stepped through, two (or three) at a time. The stroke-segmentation module may compare the three (or four) ink segments that are adjacent to the two (or three) local extrema points to a set of predefined stroke-segmentation patterns to find a closest matching pattern. Strokes are then segmented based on a stroke-segmentation rule that corresponds to the closest matching pattern. Additional stroke segmentation may be performed based on the change of curvature of the segmented ink strokes. Then, a character-recognition module performs character recognition processing by comparing the segmented ink strokes to prototype samples at least some of which have been similarly segmented.

US Patent:

7369702, May 6, 2008

Filed:

Nov 7, 2003

Appl. No.:

10/702663

Inventors:

Ahmad A. Abdulkader - Sammamish WA, US
Brian Leung - Cambridge MA, US
Henry Allan Rowley - New Castle WA, US
Qi Zhang - Redmond WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06K 9/00
G06K 9/62

US Classification:

382187, 382161, 382224

Abstract:

Input handwritten characters are classified as print or cursive based upon numerical feature values calculated from the shape of an input character. The feature values are applied to inputs of an artificial neural network which outputs a probability of the input character being print or cursive. If a character is classified as print, it is analyzed by a print character recognizer. If a character is classified as cursive, it is analyzed using a cursive character recognizer. The cursive character recognizer compares the input character to multiple prototype characters using a Dynamic Time Warping (DTW) algorithm.

US Patent:

7412095, Aug 12, 2008

Filed:

Oct 24, 2005

Appl. No.:

11/257332

Inventors:

Gregory N. Hullender - Bellevue WA, US
Ahmad Abdulkader - Sammamish WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06K 9/18

US Classification:

382186, 382177, 382187, 382189, 382294, 382295, 382296

Abstract:

An electronic ink object is preprocessed for improving handwriting recognition results. An electronic ink object, possibly having multiple lines of non-horizontal ink, is preprocessed by segmenting or separating the electronic ink object into one or more lines, computing a writing angle of each line, and rotating each line so that it is horizontal.

US Patent:

7496547, Feb 24, 2009

Filed:

Jun 10, 2005

Appl. No.:

11/150852

Inventors:

Ahmad A. Abdulkader - Redmond WA, US
Ioannis A. Drakopoulos - Redmond WA, US
Qi Zhang - Redmond WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06F 15/18

US Classification:

706 20, 706 15, 706 48, 382159

Abstract:

Handwriting recognition techniques employing a personalized handwriting recognition engine. The recognition techniques use examples of an individual's previous writing style to help recognize new pen input from that individual. The techniques also employ a shape trainer to select samples of an individual's handwriting that accurately represent the individual's writing style, for use as prototypes to recognize subsequent handwriting from the individual. The techniques also alternately or additionally employ an intelligent combiner to combine the recognition results from the personalized recognition engine and the conventional recognition engine (or engines). The combiner may use a comparative neural network to combine the recognition results from multiple recognition engines. The combiner alternately may use a rule-based system based on prior knowledge of different recognition engines.

US Patent:

7593908, Sep 22, 2009

Filed:

Jun 27, 2005

Appl. No.:

11/167734

Inventors:

Ahmad A. Abdulkader - Woodinville WA, US
Iaonnis A. Drakopoulos - Redmond WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06N 3/08

US Classification:

706 25, 382187

Abstract:

Systems and methods are provided for training neural networks and other systems with heterogeneous data. Heterogeneous data are partitioned into a number of data categories. A user or system may then assign an importance indication to each category as well as an order value which would affect training times and their distribution (higher order favoring larger categories and longer training times). Using those as input parameters, the ordered training generates a distribution of training iterations (across data categories) and a single training data stream so that the distribution of data samples in the stream is identical to the distribution of training iterations. Finally, the data steam is used to train a recognition system (e. g. , an electronic ink recognition system).

US Patent:

7646913, Jan 12, 2010

Filed:

Dec 19, 2005

Appl. No.:

11/305968

Inventors:

Ahmad A. Abdulkader - Woodinville WA, US
Kumar H. Chellapilla - Redmond WA, US
Patrice Y. Simmard - Bellevue WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06K 9/00

US Classification:

382187, 382119, 382159, 382186

Abstract:

The claimed subject matter provides a system and/or a method that facilitates analyzing and/or recognizing a handwritten character. An interface component can receive at least one handwritten character. A personalization component can train a classifier based on an allograph related to a handwriting style to provide handwriting recognition for the at least one handwritten character. In addition, the personalization component can employ any suitable combiner to provide optimized recognition.

US Patent:

7715629, May 11, 2010

Filed:

Aug 29, 2005

Appl. No.:

11/215818

Inventors:

Ahmad A. Abdulkader - Redmond WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06K 9/00
G06K 9/22

US Classification:

382186, 382187, 382188, 382189, 382313, 382314

Abstract:

Techniques for processing handwriting input based upon a user's writing style. Some techniques employ the style in which the user writes a single character, while other techniques alternately or additionally employ a group of allographs that form a handwriting style. Some implementations of these techniques, such as those implemented in writing style analysis tool, analyze one or more characters written by a user to identify a community, such as a geographic region or cultural group, to which the user's handwriting style belongs. Other implementations analyze one or more characters of a user's handwriting in order to alternately or additionally categorize the user's handwriting into a particular handwriting style. The writing style analysis tool may then provide the user with a handwriting recognition application specifically configured for that user's personal handwriting style. With still other implementations, the user's handwriting style alternately or additionally is employed to prevent a handwriting recognition application from using an aberrantly written character as training data for improving the recognition process, or to predict how a user writes other characters.