Systems and methods for blur identification and correction
AssigneeCapital One Services, LLC
InventorsJoseph James FORD, III, Thomas Oliver CANTRELL
Life of this patent
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Abstract
Methods and systems are described herein for identifying the location and nature of any blur within one or more images received as a user communication and generating an appropriate correction. The system utilizes a first machine learning model, which is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information. The system may apply a corrective action selected by the first machine learning model, which may comprise stitching blurred images together to a sharp product image and/or some other method appropriate for rectifying images received.
Claims20
A system for identifying blurring of electronically submitted images during optical document recognition received as part of a client communication, the system comprising:
- one or more processors;
- and
- a non-transitory, computer readable medium comprising instructions that when executed by the one or more processors cause operations comprising:
- receiving, from a first user, a first user communication comprising a first image, wherein the first image comprises first textual information and first non-textual information, and wherein the first textual information comprises one or more alphanumeric characters, and wherein the first non-textual information comprises one or more graphical portions;
- generating a first feature input based on the first user communication;
- inputting the first feature input into a first machine learning model, wherein the first machine learning model performs the optical document recognition, wherein the first machine learning model is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information;
- receiving a first output from the first machine learning model;
- determining based on the first output, whether the first image has a first blurred component and whether the first blurred component is located at the first textual information such that the first blurred component causes the one or more alphanumeric characters to be obfuscated during the optical document recognition;
- and
- in response to determining that the first image has a first blurred component and that the first blurred component is located at the first textual information, performing a first corrective action to the first image, wherein performing the first corrective action comprises:
- retrieving a second machine learning model, wherein the second machine learning model is trained to stitch images together to correct blurring;
- and
- processing the first image using the second machine learning model.
A method for identifying blurring of images received as part of a client communication, the method comprising:
- receiving, from a first user, a first user communication comprising a first image, wherein the first image comprises first textual information and first non-textual information;
- generating a first feature input based on the first user communication;
- inputting the first feature input into a first machine learning model, wherein the first machine learning model is trained to identify blurred components of inputted images and to determine whether the blurred components are located in portions of the inputted images comprising textual information;
- receiving a first output from the first machine learning model;
- determining based on the first output, whether the first image has a first blurred component and whether the first blurred component is located at the first textual information;
- and
- in response to determining that the first image has the first blurred component and that the first blurred component is located at the first textual information, performing a first corrective action to the first image.
A non-transitory, computer-readable medium, comprising instructions that, when executed by one or more processors, cause operations comprising:
- receiving, from a first user, a first user communication comprising a first image, wherein the first image comprises first textual information and first non-textual information;
- generating a first feature input based on the first user communication;
- inputting the first feature input into a first machine learning model, wherein the first machine learning model is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information;
- receiving a first output from the first machine learning model;
- determining based on the first output, whether the first image has a first blurred component and whether the first blurred component is located at the first textual information;
- and
- in response to determining that the first image has the first blurred component and that the first blurred component is located at the first textual information, performing a first corrective action to the first image.
Dependencies are inferred from the claim text.