Intuit, Inc.

Greg Coulombe

Richard J Becker

Igor A Podgorny

Massimo Mascaro

Matthew Cannon

Carol Ann Howe

Robert E Bamford

Pavlo Malynin

Faraz Sharafi

Horace Chan

Varadarajan Sriram

Sheldon Porcina

Kimia Hassanzadeh

Brian Joon Chung

Karla Julietta Uribe

Vitaliy Igorevich Lee

Yason Khaburzaniya

Michael Albert Miljour

Jeff W Geisler

Cole MacKenzie

Kasey L Matthews

Cole Mackenzie

The invention relates to a method. The method includes receiving a flawed input comprising a domain specific misspelling. The method further includes encoding, by an encoder machine learning model executing on a computer processor, the flawed input on a per character basis to create a context vector. The method further includes decoding, by a decoder machine learning model executing on the computer processor, the context vector on the per character basis to create a rephrased input lacking the domain specific misspelling. The method further includes presenting the rephrased input.

Misspelling correction based on deep learning architecture

The present disclosure relates to simulating the capture of images. In some embodiments, a document and a camera are simulated using a three-dimensional modeling engine. In certain embodiments, a plurality of images are captured of the simulated document from a perspective of the simulated camera, each of the plurality of images being captured under a different set of simulated circumstances within the three-dimensional modeling engine. In some embodiments, a model is trained based at least on the plurality of images which determines at least a first technique for adjusting a set of parameters in a separate image to prepare the separate image for optical character recognition (OCR).

Simulating image capture

Methods, non-transitory computer-readable storage mediums and systems for classifying features including labels or fields in digital images of forms and identifying any unclassified features utilizing a bitmap approach are provided. Each spatial template includes a set of features and a set of bitmaps, each bitmap indicating a spatial relationship between each feature and respective remaining features in each spatial template. The operations include selecting a spatial template from the spatial templates, classifying each extracted feature as one of features in the spatial template. The operations include generating at least one bitmap representing a spatial relationship for each unclassified feature in relation to classified features, comparing the at least one bitmap with each of a set of bitmaps in the selected spatial template, and identifying the at least one unclassified feature as one of features in the spatial template based on a result of the comparison.

Feature classification with spatial analysis

A method and system provides augmented OCR data to a user of a financial system. The method and system include receiving image data related to an image of a financial document of the user and generating OCR data based on the image data. The method and system further include receiving financial data related to the financial document, analyzing the financial document, and generating the augmented OCR data based on the OCR data and the financial data.

Data accuracy in OCR by leveraging user data and business rules to improve data accuracy at field level

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Edmonton, Canada

Greg Coulombe