Intuit, Inc.

Peijun Chiang

Vijay S Yellapragada

Sreeneel K Maddika

Daniel Juhyung Lee

Shailesh K Soliwal

Jason Hall

Eugene Krivopaltsev

Desiree D G Gosby

Praveen R Swadi

Aspects of the present disclosure provide methods and apparatuses for processing a digital image of a document, for example, to determine whether the document is a long document. An exemplary method generally includes obtaining a plurality of digital images of the document, segmenting at least a first digital image of the plurality of images into pixels associated with a foreground of the first digital image and pixels associated with a background of the first digital image, detecting a plurality of contours in the segmented first digital image, deciding, for each detected contour of the plurality of contours, whether that contour is an open contour or a closed contour, and determining that one or more sides of the document is out of bounds based, at least in part, on the decisions.

Out-of-bounds detection for a document in a live camera feed

Aspects of the present disclosure provide methods and apparatuses for processing a digital image of a document, for example, to determine whether the document is a long document. An exemplary method generally includes obtaining a plurality of digital images of the document, segmenting at least a first digital image of the plurality of images into pixels associated with a foreground of the first digital image and pixels associated with a background of the first digital image, detecting a plurality of contours in the segmented first digital image, deciding, for each detected contour of the plurality of contours, whether that contour is an open contour or a closed contour, and determining that one or more sides of the document is out-of-bounds based, at least in part, on the decisions.

Out-of bounds detection of a document in a live camera feed

The present disclosure relates to optical character recognition using captured video. According to one embodiment, using a first image in stream of images depicting a document, the device extracts text data in a portion of the document depicted in the first image and determines a first confidence level regarding an accuracy of the extracted text data. If the first confidence level satisfies a threshold value, the device saves the extracted text data as recognized content of the source document. Otherwise, the device extracts the text data from the portion of the document as depicted in one or more second images in the stream and determines a second confidence level for the text data extracted from each second image until identifying one of the second images where the second confidence level associated with the text data extracted from the identified second image satisfies the threshold value.

Optical character recognition (OCR) accuracy by combining results across video frames

The present disclosure relates to optical character recognition, and more specifically techniques for detecting font size in a digital image. Accordingly to one embodiment, a client device receives a digital image of a document having one or more textual components. The client device finds one or more contours bounding the one or more textual components in the digital image of the document. The client device detects a font size for text contained in the digital image using the one or more contours. The client device extracts the text from the digital image upon detecting that the detected font size is above a defined threshold value.

Detecting font size in a digital image

Techniques are disclosed for performing optical character recognition (OCR) by identifying a template based on a hash of a document. One embodiment includes a method for identifying a template associated with an image. The method includes receiving a digital image, a portion of the image depicting a first document, and extracting the portion of the image. The method further includes scaling the portion of the image and generating a first hash from the scaled image. The method further includes comparing the first hash to a set of hashes, each corresponding to a template. The method further includes selecting a first template as corresponding to the first document based on comparing the first hash to the set of hashes and extracting one or more sections of the portion of the image based on the selected first template. The method further includes performing OCR on the extracted one or more sections.

Optical character recognition utilizing hashed templates

Aspects of the present disclosure provide methods and apparatuses for processing a digital image of a document, for example, to determine whether the document is a long document. An exemplary method generally includes obtaining a plurality of digital images of the document, determining a type of the document, loading one or more pre-defined metrics associated with the document based on the determined type of the document, determining one or more characteristics of the document based on one or more analyses performed on the plurality of digital images of the document, comparing the one or more characteristics of the document with the one or more pre-defined metrics, and determining the document to be a long document based, at least in part, on the comparison.

Detecting long documents in a live camera feed

Aspects of the present disclosure provide methods and apparatuses for detecting duplicate copies of a form in an image of a document. An exemplary method generally includes obtaining a first digital image of a document, performing one or more transformations on the first digital image, determining one or more rectangles in the transformed first digital image, identifying at least a first duplicate copy of the form being depicted in the first digital image based, at least in part, on the detected one or more rectangles, and generating, based on the identified duplicate copy of the form, a notification that the first digital image includes at least the first duplicate copy of the form.

Identification of duplicate copies of a form in a document

The present disclosure relates to the extraction of text from an image including a depiction of a document. According to one embodiment, a mobile device receives an image depicting a document. The mobile device identifies a plurality of text areas in the document and identifies a midpoint of each of the plurality of text areas in the document. The mobile device detects one or more lines of text in the document including a plurality of text areas, where the plurality of text areas included in a line of text are associated with a midpoint having a coordinate within a threshold number of pixels on one axis in a two-dimensional space. Based on an orientation of the detected one or more lines of text, the mobile device determines a probable orientation of the document and extracts text from the image based on the determined probable orientation of the document.

Detecting orientation of textual documents on a live camera feed

Techniques are disclosed for facilitating optical character recognition (OCR) by identifying one or more regions in an electronic document to perform the OCR. For example a method for identifying information in an electronic document includes obtaining a set of training documents for each template of a plurality of templates for the electronic document, extracting spatial attributes for at least a first label region and at least a first corresponding value region from the set, and training a classifier model based on the extracted spatial attributes, wherein the classifier model is used to identify the information in the electronic document. The spatial attributes represent a position of at least the first label region and at least the first value region within the electronic document.

Performing optical character recognition using spatial information of regions within a structured document

The disclosed embodiments provide a system that provides a user interface of a native application for a portable electronic device. During operation, the system provides an environment for customizing the user interface using one or more custom views. The one or more custom views may include a set of user-interface components, a layout of the user-interface components, and a configuration of a user-interface component from the set of user-interface components. Next, the system enables use of the one or more custom views with the native application through the environment independently of a platform of the native application.

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Mountain View, CA, United States of America

Peijun Chiang