Arjun Kumar

US Patent:

20090116755, May 7, 2009

Filed:

Nov 6, 2008

Appl. No.:

12/266454

Inventors:

Depankar Neogi - Wilmington MA, US
Steven K. Ladd - North Andover MA, US
Arjun Kumar - Boxborough MA, US
Dilnawaj Ahmed - Bangalore, IN

Assignee:

Copanion, Inc. - Andover MA

International Classification:

G06K 9/62

US Classification:

382224

Abstract:

A method in a document analysis system automatically extracts image and text features from each received electronic document and compares the extracted features with feature sets associated with each category of document to determine whether the document is recognizable as belonging to a document category. If an electronic document is recognized as belonging to one of the document categories, the method classifies the electronic document as belonging to that document category. If, however, an electronic document is unrecognized, the method submits the unrecognized document to a learning phase, in which the unrecognized document is presented to a human trainer for manual classification of the unrecognized electronic document into a document category, and automatically modifies at least one of the features and the weights of the feature set of the document category corresponding to the manually-classified electronic document using the automatically extracted features of the manually-classified document.

US Patent:

20090116756, May 7, 2009

Filed:

Nov 6, 2008

Appl. No.:

12/266469

Inventors:

Depankar Neogi - Wilmington MA, US
Steven K. Ladd - North Andover MA, US
Arjun Kumar - Boxborough MA, US
Dilnawaj Ahmed - Bangalore, IN

Assignee:

Copanion, Inc. - Andover MA

International Classification:

G06K 9/62

US Classification:

382224

Abstract:

A method of training a document analysis system that automatically extracts image and text features from each received electronic document and compares the extracted features with feature sets associated with each document category is provided. If an electronic document is recognized as belonging to one of the document categories with predetermined confidence, the method classifies the electronic document as being of that one document category. If an electronic document is not recognized as belonging to one of the document categories with predetermined confidence, however, the method submits the unrecognized document to a training phase in which the document is recognized as belonging to a document category and automatically modifies at least one of the features and the weights of the features of the feature set for the document category for the now-recognized document.

US Patent:

20090201541, Aug 13, 2009

Filed:

Jan 9, 2009

Appl. No.:

12/351302

Inventors:

Depankar NEOGI - Wilmington MA, US
Steven K. LADD - North Andover MA, US
Arjun KUMAR - Boxborough MA, US
Matthew DUGGAN - Boston MA, US

Assignee:

COPANION, INC. - Andover MA

International Classification:

G06F 3/12
H04N 1/04

US Classification:

358 115, 358474

Abstract:

A method of controlling a scanner to improve automatic recognition and classification of scanned physical documents for a document analysis system, which receives and processes jobs containing at least one electronic document from a plurality of users to automatically recognize and classify the job documents into document categories, is disclosed. The method comprises, using a scan control system, obtaining the capability of, and existing scanner settings for, the scanner upon receiving a command to initiate scanning of physical documents; saving the existing scanner settings of the scanner; automatically commanding the scanner to use new scanner settings, wherein the new scanner settings are selected in accordance with the capability of the recognition system; commanding the scanner to begin scanning operation with the new scanner settings; and automatically resetting the scanner settings of the scanner back to the saved existing scanner settings upon completing of the scanning operation.

US Patent:

20110255789, Oct 20, 2011

Filed:

Jan 14, 2011

Appl. No.:

13/007443

Inventors:

Depankar NEOGI - Wilmington MA, US
Steven K. LADD - North Andover MA, US
Girish WELLING - Nashua NH, US
Arjun KUMAR - Boxborough MA, US
Vartika SINGH - Lawrence MA, US
Matthew DUGGAN - Boston MA, US
Tushar MAHATA - Jersey City NJ, US
Xiaobin YANG - Woburn MA, US
Janice O'NEIL - Nottingham NH, US
Nirupam SARKAR - Westford MA, US
Gopal KRISHNA - Bellevue WA, US

Assignee:

COPANION, INC. - Andover MA

International Classification:

G06K 9/46

US Classification:

382190

Abstract:

A method of automatically extracting data from an electronic document containing a plurality of layout features through progressive refinement is provided. The method includes: analyzing each document to automatically extract images and text features wherein each document includes at least two features that are related to each other, and wherein said analyzing compares extracted features with a first search space of candidate features to try and recognize the extracted features; if one of the at least two related features is not recognized and at least one feature is recognized, selecting a second search space of candidate features in response thereto and in response to predefined rules about the relationship between the two features; and comparing the unrecognized feature with said selected second search space.

US Patent:

20110258150, Oct 20, 2011

Filed:

Jan 14, 2011

Appl. No.:

13/007430

Inventors:

Depankar NEOGI - Wilmington MA, US
Steven K. LADD - North Andover MA, US
Girish WELLING - Nashua NH, US
Arjun KUMAR - Boxborough MA, US
Vartika SINGH - Lawrence MA, US
Matthew DUGGAN - Boston MA, US
Tushar MAHATA - Jersey City NJ, US
Xiaobin YANG - Woburn MA, US
Janice O'NEIL - Nottingham NH, US
Nirupam SARKAR - Westford MA, US
Gopal KRISHNA - Bellevue WA, US

Assignee:

COPANION, INC. - Andover MA

International Classification:

G06F 15/18

US Classification:

706 12

Abstract:

A method of training a document analysis system to extract data from documents is provided. The method includes: automatically analyzing images and text features extracted from a document to associate the document with a corresponding document category; comparing the extracted text features with a set of text features associated with corresponding category of the document, in which the set of text features includes a set of characters, words, and phrases; if the extracted features are found to consist of the characters, words, and phrases belonging to the set of text features associated with the corresponding document category, storing the extracted text features as the data contained in the corresponding document; and, if the extracted text features are found to include at least one text feature that does not belong to the set of text features associated with the corresponding document category, submitting the unrecognized text features to a training phase.

US Patent:

20090116736, May 7, 2009

Filed:

Nov 6, 2008

Appl. No.:

12/266462

Inventors:

Depankar Neogi - Wilmington MA, US
Steven K. Ladd - North Andover MA, US
Dilnawaj Ahmed - Bangalore, IN
Arjun Kumar - Boxborough MA, US
Tushar Mahata - Bangalore, IN

Assignee:

Copanion, Inc. - Andover MA

International Classification:

G06K 9/62

US Classification:

382159

Abstract:

A document analysis system that automatically classifies documents by recognizing in each document distinctive features comprises a document acquisition system, a document recognition training system, a document classification system, a document recognition system, and a job organization system. The document acquisition system receives jobs wherein each job containing at least one electronic document. The document feature recognition system automatically extracts image and text features from each received document. The document classification system automatically classifies recognized electronic documents by finding the best match between the extracted features of each of the document and feature sets associated with each category of document. The document recognition training system automatically trains the feature set for each corresponding category of documents, wherein the training system using extracted features of unrecognized documents automatically modifies the feature set for a document category. The job organization system automatically organizes each job according to the document categories it contains.