Systems and methods for identification document processing and business workflow integration

Abstract

A method includes: receiving or capturing an image comprising an identity document (ID) using a mobile device; classifying the ID; analyzing the ID based at least in part on the ID classification; determining at least some identifying information from the ID; at least one of building an ID profile and updating the ID profile, based at least in part on the analysis; providing at least one of the ID and the ID classification to a loan application workflow and / or a new financial account workflow; and driving at least a portion of the workflow based at least in part on the ID and the ID classification. Corresponding systems and computer program products are also disclosed.

Description

PRIORITY CLAIM[0001]This application is a continuation of U.S. application Ser. No. 14 / 220,016, filed Mar. 19, 2014, which is a continuation-in-part of U.S. application. Ser. No. 13 / 740,123, filed Jan. 11, 2013, which claims the benefit of U.S. Provisional Application No. 61 / 586,062, filed Jan. 12, 2012, and U.S. Provisional Application No. 61 / 720,958, filed Oct. 31, 2012, each of which are herein incorporated by reference.RELATED APPLICATIONS[0002]This application is related to U.S. Pat. No. 8,526,739 U.S. patent application Ser. No. 13 / 802,226, filed Mar. 13, 2013 and from U.S. Provisional Patent Application No. 61 / 780,747, filed Mar. 13, 2013, each of which are herein incorporated by reference.FIELD OF INVENTION[0003]The present invention relates to image capture and image processing, and more particularly to capturing and processing digital images of identity documents (IDs) in connection with performing part or all of a business workflow using a mobile device.BACKGROUND[0004]Di...

AI Technical Summary

Problems solved by technology

A major challenge in transitioning conventional document capture and processing techniques is the limited processing power and image resolution achievable using hardware currently available in mobile devices.

These limitations present a significant challenge because it is impossible or impractical to process images captured at resolutions typically much lower than achievable by a conventional scanner.

As a result, conventional scanner-based processing algorithms typically perform poorly on digital images captured using a mobile device.

In addition, the limited processing and memory available on mobile devices makes conventional image processing algorithms employed for scanners prohibitively expensive in terms of computational cost.

Attempting to process a conventional scanner-based image processing algorithm takes far too much time to be a practical application on modern mobile platforms.

A still further challenge is presented by the nature of mobile capture components (e.g. cameras on mobile phones, tablets, etc.).

Where conventional scanners are capable of faithfully representing the physical document in a digital image, critically maintaining aspect ratio, dimensions, and shape of the physical document in the digital image, mobile capture components are frequently incapable of producing such results.

Specifically, images of documents captured by a camera present a new line of processing issues not encountered when dealing with images captured by a scanner.

Additionally, with camera capture, nonlinear distortions may be introduced because of the camera optics.

Other major challenges unique to capturing image and / or video data utilizing a camera or array of cameras may include variable illumination conditions, for instance non-uniform lighting conditions that may generate shadows on objects depicted in images, presence of specular lights which may generate glare, etc. as would be understood by skilled artisans upon reading these disclosures.

In addition, utilizing cameras to capture image and / or video data introduces challenges with respect to distinguishing an object of interest from relatively complex backgrounds as compared to the typical background for a flat-bed scanner (which exhibits characteristics that are well-known and relatively immutable, for instance a single background texture and color for the scanner background).

As a result, clustered background makes page segmentation difficult and challenging as compared to scenarios typically encountered using scanner-generated image data.

To date, these applications have been limited in scope to simple transactions leveraging conventions and standards unique to very narrow aspects of the financial services industry.

Reliance on such conventional standards and industry-specific practices allows high-fidelity and high-performance in the very limited scope to which those standards and practices apply, but unfortunately limit the applicability of the underlying technology to only those narrow fields.

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