United Services Automobile Association (Usaa)

Adam Spencer Newman

Reynaldo Medina, Iii

Seth E Ethington

Aaron Robert Kay

Robert G Wood

Nandhini Sridaran

Dylan Patterson

Lawrence Z Lu

William Barfield

Mario Gutierrez

Joel Andrew McKay

David Edward South

Christopher Nephi Clark

An image processing system or electronic device may implement processing circuitry. The processing circuitry may receive an image, such as financial document image. The processing circuitry may determine a character count for the financial document image or particular portions of the financial document image without recognizing any particular character in the financial document image. In that regard, the processing circuitry may determine a top left score for pixels in the financial document, the top left score indicating or representing a likelihood that a particular pixel corresponds to a top left corner of a text character. The processing circuitry may also determine top right score for image pixels. Then, the processing circuitry may identify one or more text chunks using the top left and top rights scores for pixels in the financial document image. The processing circuitry may determine a character count for the identified text chunks.

Character count determination for a digital image

The AUTOMATIC REMOTE DEPOSIT IMAGE PREPARATION APPARATUSES, METHODS AND SYSTEMS (hereinafter 'MultiCrop') transforms uploaded check images and check deposit information inputs via MultiCrop components image into deposit confirmation outputs. For example, in one embodiment, a user may employ a personal computer connected to a web camera, and/or a smartphone with a built-in camera to initiate the remote deposit by holding a check in front of the camera, and the MultiCrop may capture images of the check and send them to a financial institution for deposit processing.

Automatic remote deposit image preparation apparatuses, methods and systems

Techniques for detecting counterfeit checks include using sensors to determine correspondence between items detected on a check using machine vision. Correspondence between different items on a received check is used to generate a check score, which is compared to a risk-based threshold to determine how a transaction involving the check should be handled.

Systems and methods for counterfeit check detection

Techniques for detecting counterfeit checks include using sensors to determine correspondence between a portion of sensed check information and related information; analyzing behavior related to accounts to a check deposit transaction; and controlling check depositing through user interface controls or communication. These techniques can be employed individually or in combination.

Systems and methods are provided herein that can include capturing a plurality of images of a document, analyzing the plurality of images to identify a plurality of acceptable portions within the plurality of images, combining the plurality of acceptable portions to generate a composite image of the document, and transmitting the composite image to a depository. Additionally or alternatively, the systems and methods can include capturing an image of a document, identifying a designated portion of the captured image of the document that fails to satisfy a predetermined image quality criteria, recapturing an image of only the designated portion of the document, generating a composite image from the captured image of the document and the recaptured image of only a portion of the document, and transmitting the composite image of the document to a depository via a communication pathway.

Growing community of inventors

San Antonio, TX, United States of America

Adam Spencer Newman