Remote deposit of checks can be facilitated by a financial institution. A customer's general purpose computer and image capture device may be leveraged to capture an image of a check and deliver the image to financial institution electronics. Additional data for the transaction may be collected as necessary. The transaction can be automatically accomplished utilizing the images and data thus acquired.

Disclosed are methods and systems for using artificial intelligence (AI) for image recognition by using predefined coordinates to extract a portion of a received image, the extracted portion comprising a word to be identified having at least a first letter and a second letter; executing an image recognition protocol to identify the first letter; when the server is unable to identify the second letter, the server executes an AI model having a nodal data structure to identify the second letter based upon the identified first letter, the nodal data structure comprising a set of nodes where each node represents a letter, each node connected to at least one other node, wherein connection of a first node to a second node corresponds to a probability that a letter corresponding to the second node is used in a word subsequent to a letter corresponding to the first node.

The technology described herein generates a unique identifier for a visual media that comprises pre-printed visual indications on the visual media and a user's handwritten signature. The location of the signature on the visual media can be determined by including preprinted fiducial marks on the visual media. The fiducial markers act as landmarks that allow the size and location of the signature to be determined in absolute terms. The unique identifier is then stored in computer memory on a user-experience server. The user-experience server can associate the unique identifier with a digital asset, such as an image or video, designated by the user. When the unique identifier is provided to the user-experience server a second time, the digital asset can be retrieved and output to the computing device that provided the unique identifier.

Two models are first trained and then test images are applied to the two trained models in an effort to detect signature forgeries. The first model is trained with pairs of signature images and the resultant trained model is capable of detecting blind forgeries. The second model is trained with triplets of signature images and is capable of detecting skilled signature forgeries. After the two models are trained, test images are applied to the models and determinations are made as to whether a blind or skilled forgery is present.

A system for extraction and verification of handwritten signatures from arbitrary documents. The system comprises one or more computing devices configured to: receive a digital image of a document; perform a dilating transformation via convolution matrix on the digital image to obtain a modified image; determine a plurality of regions of connected markings in the digital image; based at least in part on a pixel density or proximity to an anchor substring of each region, determine whether any region contains any handwritten signature; extract first image data of the region containing a handwritten signature from the digital image; retrieve second image data of a confirmed example signature for a purported signer of the handwritten signature; and based on a comparison of the first image data with the second image data, forward a determination of whether the first image data and second image data are similar.

The method, system, and non-transitory computer-readable medium embodiments described herein are directed to verifying identification documents. In various embodiments, a server may receive a first image of a front-side of an identification document. The server may extract a first feature of the front-side of the identification document from the first image using optical character recognition (OCR) and identify a first environmental feature from the first image. The server may receive a second image of a backside of the identification document and identify a second feature of the backside of the identification document from the second image. The server may also identify a second environmental feature from the second image. The server may verify the identification document by confirming that the first feature matches the second feature and the first environmental feature matches the second environmental feature.

The invention concerns an identity document comprising a visual representation of encoded data, the encoded data allowing a capturing device to determine location of at least a part of the outlines of a region-of-interest of the identity document, relative to at least one reference point of the visual representation, the region-of-interest being different from the visual representation.

A system and a method are disclosed for enabling pictorial content to be added to a secure document. In an embodiment, a secure document tool receives a request, from an administrator of the secure document, to enable modification of a region of the secure document with an addition of pictorial content, the secure document configured to prevent modification of contents of the secure document by a signer, the secure document enabled to accept a signature on the secure document by the signer. The secure document tool receives, from the signer, a command to add pictorial content to the region, and responsively adds the pictorial content to the region. The secure document tool receives from the signer, a signature on the secure document, and responsively disables the secure document from accepting further modifications.

Remote deposit of checks can be facilitated by a financial institution. A customer's general purpose computer and image capture device may be leveraged to capture an image of a check and deliver the image to financial institution electronics. Additional data for the transaction may be collected as necessary. The transaction can be automatically accomplished utilizing the images and data thus acquired.

Methods, systems, and computer program products are provided for signature verification. Signature verification may be provided for target signatures using genuine signatures. A signature verification model pipeline may extract features from a target signature and a genuine signature, encode and submit both to a neural network to generate a similarity score, which may be repeated for each genuine signature. A target signature may be classified as genuine, for example, when one or more similarity scores exceed a genuine threshold. A signature verification model may be updated or calibrated at any time with new genuine signatures. A signature verification model may be implemented with multiple trainable neural networks (e.g., for feature extraction, transformation, encoding, and/or classification).