Michael C 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:

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:

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.

US Patent:

8408917, Apr 2, 2013

Filed:

Mar 3, 2011

Appl. No.:

13/040213

Inventors:

Michael Perrone - Seaford NY, US

International Classification:

G09B 19/00

US Classification:

434226

Abstract:

The present invention relates generally to apparatus for training the art of forcible door entry for emergency personnel such as firefighters and, in particular, a reusable forcible entry door training prop adapted to simulate a locked door and allow individuals to conduct training in forcible entry techniques for different door types and mounting configurations (e. g. , opening inward or outward, left hand opening or right hand opening, steel jam or wood jams).

US Patent:

8419435, Apr 16, 2013

Filed:

Oct 6, 2011

Appl. No.:

13/267822

Inventors:

Michael Perrone - Seaford NY, US

International Classification:

G09B 19/00

US Classification:

434226

Abstract:

Apparatus for training the art of forcible door entry for emergency personnel such as firefighters and, in particular, a reusable forcible entry door training apparatus that allows individuals to simulate forcible entry of outward opening metal doors mounted in metal frames.

US Patent:

20130224700, Aug 29, 2013

Filed:

Mar 29, 2013

Appl. No.:

13/853085

Inventors:

Michael Perrone - Seaford NY, US

International Classification:

A62C 99/00

US Classification:

434226

Abstract:

The present invention relates generally to apparatus for training the art of forcible door entry for emergency personnel such as firefighters and, in particular, a reusable forcible entry door training prop adapted to simulate a locked door and allow individuals to conduct training in forcible entry techniques for different door types and mounting configurations (e.g., opening inward or outward, left hand opening or right band opening, steel jam or wood jams).

US Patent:

20130224701, Aug 29, 2013

Filed:

Mar 29, 2013

Appl. No.:

13/853086

Inventors:

Michael Perrone - Seaford NY, US

International Classification:

G09B 25/00

US Classification:

434226

Abstract:

Apparatus for training the art of forcible door entry for emergency personnel such as firefighters and, in particular, a reusable forcible entry door training apparatus that allows individuals to simulate forcible entry of outward opening metal doors mounted in metal frames.

Address:

674 Wyngate Dr W, Valley Stream, NY 11580

Phone:

5162703670

VIN:

1HGCM56887A173010

Make:

HONDA

Model:

ACCORD

Year:

2007