Michael M Perrone

US Patent:

6567548, May 20, 2003

Filed:

Jan 29, 1999

Appl. No.:

09/240362

Inventors:

Krishna S. Nathan - New York NY
Michael P. Perrone - Yorktown NY
John F. Pitrelli - Danbury CT

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06K 918

US Classification:

382186, 382159, 382179, 382187

Abstract:

A handwriting recognition system and method whereby various character sequences (which are typically âslurredâ together when handwritten) are each modelled as a single character (âcompound character modelâ) so as to provide increased decoding accuracy for slurred handwritten character sequences. In one aspect of the present invention, a method for generating a handwriting recognition system having compound character models comprises the steps of: providing an initial handwriting recognition system having individual character models; collecting and labelling a set of handwriting data; aligning the labelled set of handwriting data; generating compound character data using the aligned handwriting data; and retraining the initial recognition system with the compound character data to generate a new recognition system having compound character models. Once these compound character models are trained, they may be used to accurately decode slurred handwritten character sequences for which compound character models were previously generated. Once recognized, the compound characters are expanded into the constituent individual characters comprising the compound character.

US Patent:

6603881, Aug 5, 2003

Filed:

Oct 23, 2001

Appl. No.:

10/047152

Inventors:

Michael P. Perrone - Yorktown NY
Eugene H. Ratzlaff - Hopewell Junction NY

Assignee:

International Business Machines Corporation - Armonk NJ

International Classification:

G06K 918

US Classification:

382186, 382181, 382187, 382189, 382202, 382225

Abstract:

Systems and methods for reordering unconstrained handwriting data using both spatial and temporal interrelationships prior to recognition, and for spatially organizing and formatting machine recognized transcription results. The present invention allows a machine recognizer to generate and present a full and accurate transcription of unconstrained handwriting in its correct spatial context such that the transcription output can appear to âmirrorâ the corresponding handwriting.

US Patent:

6897851, May 24, 2005

Filed:

Jan 5, 2001

Appl. No.:

09/755787

Inventors:

Paul Robert Carini - Sherman CT, US
Paul Turquand Keyser - Mount Kisco NY, US
Michael Peter Perrone - Yorktown NY, US
David A. Sawin - Durham NC, US
Jeffrey S. Schaffer - Ridgefield CT, US
Jayashree Subrahmonia - White Plains NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G09G005/00

US Classification:

345173

Abstract:

Several methods, and related apparatus, are provided for the entry of formatted ink data (i. e. , electronic ink) such that individual items in the data may be parsed and recognized more effectively. Each method allows users to enter formatted ink data in-line, which can then be recognized with constraints and parsed for use in other application programs or databases. In addition, a method is provided for allowing user-specialization of any of these entry methods (or other similar) methods. Note that in any of these methods, the user may send the formatted ink either to the default ink-processing application, or else directly to another application or database.

US Patent:

7260779, Aug 21, 2007

Filed:

Sep 30, 2005

Appl. No.:

11/240605

Inventors:

Paul Turquand Keyser - Mount Kisco NY, US
Michael Peter Perrone - Yorktown NY, US
Eugene H. Ratzlaff - Hopewell Junction NY, US
Jayashree Subrahmonia - White Plains NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06F 15/00

US Classification:

715525, 382187

Abstract:

In one aspect of the present invention, page breaks are identified in the following manner. A set of ink data and a document description are processed by a variety of scoring methods, each of which generates a score for each possible insertion point in the ink. These scores are combined to produce a ranked list of hypothesized page breaks for the corresponding ink data. This ranked list is then used either to insert page breaks automatically using a predefined threshold to determine a cut-off in the list; or to present, on-line, to a human for verification/approval; or a mixture of the two based on two thresholds: one for automatic insertion and the other for human verification. It is to be understood not all scoring methods need be used, that is, one or more of the scoring methods may be used as needed.

US Patent:

7343041, Mar 11, 2008

Filed:

Feb 19, 2002

Appl. No.:

10/079763

Inventors:

Thomas Yu-Kiu Kwok - Washington Township NJ, US
Michael Peter Perrone - Yorktown Heights NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06K 9/00
G06K 9/62

US Classification:

382187, 382224

Abstract:

A handwritten word is transcribed into a list of possibly correct transcriptions of the handwritten word. The list contains a number of text words, and this list is compared with previously stored set of lists of text words. Based on a metric, one or more nearest neighbor lists are selected from the set. A decision is made, according to a number of combination rules, as to which text word in the nearest neighbor lists or the recently transcribed list is the best transcription of the handwritten word. This best transcription is selected as the appropriate text word transcription of the handwritten word. The selected word is compared to a true transcription of the selected word. Machine learning techniques are used when the selected and true transcriptions differ. The machine learning techniques create or update rules that are used to determine which text word of the nearest neighbor lists or the recently transcribed list is the correct transcription of the handwritten word.

US Patent:

7400277, Jul 15, 2008

Filed:

Apr 6, 2004

Appl. No.:

10/818567

Inventors:

Michael P. Perrone - Yorktown Heights NY, US
Eugene H. Ratzlaff - Hopewell Junction NY, US
Jianying Hu - Bronx NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

H03M 7/00

US Classification:

341107, 341106, 341 65, 341 67

Abstract:

The present invention relates to a method, computer program product and system for the compression of a probability table and the reconstruction of one or more probability elements using the compressed data and method. After determining a probability table that is to be compressed, the probability table is compressed using a first probability table compression method, wherein the probability table compression method creates a first compressed probability table. The first compressed probability table contains a plurality of probability elements. Further, the probability table is compressed using a second probability table compression method, wherein the probability table compression method creates a second compressed probability table. The second compressed probability table containing a plurality of probability elements. A first probability element reconstructed using the first compressed probability table is thereafter merged with a second probability element reconstructed using the second compressed probability table in order to produce a merged probability element.

US Patent:

7627596, Dec 1, 2009

Filed:

Feb 19, 2002

Appl. No.:

10/079741

Inventors:

Thomas Yu-Kiu Kwok - Washington Township NJ, US
James Randal Moulic - Poughkeepsie NY, US
Kenneth Blair Ocheltree - Ossining NY, US
Michael Peter Perrone - Yorktown Heights NY, US
John Ferdinand Pitrelli - Danbury CT, US
Eugene Henry Ratzlaff - Hopewell Junction NY, US
Gregory Fraser Russell - Yorktown Heights NY, US
Jayashree Subrahmonia - White Plains NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06F 17/00

US Classification:

707102, 715268

Abstract:

The techniques in the present invention allow both text and handwritten queries, and the queries can be single-word or multiword. Generally, each handwritten word in a handwritten document is converted to a document stack of words, where each document stack contains a list of text words and a word score of some type for each text word in the list. The query is also converted to one or more stacks of words. A measure is determined from each query and document stack. Documents that meet search criteria in the query are then selected based on the query and the values of the measures. The present invention also performs multiple recognitions, with multiple recognizers, on a handwritten document to create multiple recognized transcriptions of the document. The multiple transcriptions are used for document retrieval. In another embodiment, a single transcription is created from the multiple transcriptions, and the single transcription is used for document retrieval.

US Patent:

7697760, Apr 13, 2010

Filed:

Jan 11, 2008

Appl. No.:

11/972913

Inventors:

Thomas Yu-Kiu Kwok - Washington Township NJ, US
Michael Peter Perrone - Yorktown Heights NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06K 9/00
G06K 9/18
G06K 9/72

US Classification:

382186, 382187, 382229

Abstract:

A handwritten word is transcribed into a list of possibly correct transcriptions of the handwritten word. The list contains a number of text words, and this list is compared with previously stored set of lists of text words. Based on a metric, one or more nearest neighbor lists are selected from the set. A decision is made, according to a number of combination rules, as to which text word in the nearest neighbor lists or the recently transcribed list is the best transcription of the handwritten word. This best transcription is selected as the appropriate text word transcription of the handwritten word. The selected word is compared to a true transcription of the selected word Machine learning techniques are used when the selected and true transcriptions differ. The machine learning techniques create or update rules that are used to determine which text word of the nearest neighbor lists or the recently transcribed list is the correct transcription of the handwritten word.

Address:

2321 Kingsland Ave, Bronx, NY 10469

Phone:

2014109824

VIN:

WBAVA33587KX83351

Make:

BMW

Model:

3 SERIES

Year:

2007