According to the present disclosure, a handwritten signature to be authenticated is received, a plurality of pieces of signature behavioral characteristic information are extracted, all of the plurality of the pieces of the extracted signature behavioral characteristic information are applied to each of first and second signature authentication algorithms using different techniques to analyze a degree of matching between the received handwritten signature and a registered handwritten signature, results of analysis performed by the first and second signature authentication algorithms are combined to adjust a false rejection rate and a false acceptance rate, and whether handwritten signature authentication succeeds is finally determined.

Methods and systems for adaptive, template-independent handwriting extraction from images using machine learning models and without manual localization or review. For example, the system may receive an input image, wherein the input image comprises native printed content and handwritten content. The system may process the input image with a model to generate an output image, wherein the output image comprises extracted handwritten content based on the native handwritten content. The system may process the output image to digitally recognize the extracted handwritten content. The system may generate a digital representation of the input image, wherein the digital representation comprises the native printed content and the digitally recognized extracted handwritten content.

A method and system of signature verification that includes receiving a signature-in-question, extracting at least one of an image of the signature-in-question and a trajectory of the signature-in-question from the received signature-in-question, accessing a reference signature, extracting at least one of a reference image and a reference trajectory from the accessed reference signature, comparing the at least one of the image of the signature-in-question and trajectory of the signature-in-question to the at least one of the reference image and reference trajectory, and determining whether signature-in-question is authentic based on the comparison.

A document management system performs name matching operations to validate an identity claimed by a recipient of a document. To validate a claimed identity the document management system compares name data obtained from an identity data source with name data corresponding to a recipient entity of a document to determine whether name features of the identity source name data match recipient name data. The name matching operations may include applying a set of name matching rules to the identity source name data and the recipient name data to determine whether features that differ between the identity source name data and the recipient name data are acceptable alternative representations. Responsive to successfully validating the identity, the system may authorize the recipient to perform actions on the document. Identity source name data may be received from a variety of identity data sources, such as an identity document or a trusted service provider.

A system, method and writing apparatus disclosed herein enables a physical handwritten mark (such as “wet” signature) to be encoded with user biometric characteristics which are unique to the person making the handwritten mark. A system and method are described that may utilize a signature created by the writing apparatus.

The present disclosure relates to a handwritten signature authentication system and method. More particularly, the present disclosure provides a system and method of authenticating a handwritten signature based on dynamic movement tracking of spatial-division segments, in which handwritten signature authentication is performed by handwritten signature characteristics information (i.e. overall handwritten signature block characteristics information (Q), overall spatial-division segment block characteristics information (V), overall spatial-division segment block position information (P) including sub-segment block position information, and overall spatial-division segment block correlation characteristics information (C), spatial-division segment dynamic behavioral characteristics information, overall segment dynamic behavioral characteristics information, segment transition dynamic behavioral characteristics information generated during a segment transition while the handwritten signature is made, and an overall segment transition dynamic behavioral characteristics information) based on spatial-division segment blocks including handwritten signature images (referred to as “segments” or “segment images”) divided by a spatial unit.

Biometric data are obtained from biometric sensors on a stand-alone computing device, which may contain an ASIC, connected to or incorporated within it. The computing device and ASIC, in combination or individually, capture biometric samples, extract biometric features and match them to one or more locally stored, encrypted templates. The biometric matching may be enhanced by the use of an entered PIN. The biometric templates and other sensitive data at rest are encrypted using hardware elements of the computing device and ASIC, and/or a PIN hash. A stored obfuscated Password is de-obfuscated and may be released to the authentication mechanism in response to successfully decrypted templates and matching biometric samples. A different de-obfuscated password may be released to authenticate the user to a remote or local computer and to encrypt data in transit. This eliminates the need for the user to remember and enter complex passwords on the device.

A method and system of signature verification that includes receiving a signature-in-question, extracting at least one of an image of the signature-in-question and a trajectory of the signature-in-question from the received signature-in-question, accessing a reference signature, extracting at least one of a reference image and a reference trajectory from the accessed reference signature, comparing the at least one of the image of the signature-in-question and trajectory of the signature-in-question to the at least one of the reference image and reference trajectory, and determining whether signature-in-question is authentic based on the comparison.

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).

Biometric data are obtained from biometric sensors on a stand-alone computing device, which may contain an ASIC, connected to or incorporated within it. The computing device and ASIC, in combination or individually, capture biometric samples, extract biometric features and match them to one or more locally stored, encrypted templates. The biometric matching may be enhanced by the use of an entered PIN. The biometric templates and other sensitive data at rest are encrypted using hardware elements of the computing device and ASIC, and/or a PIN hash. A stored obfuscated Password is de-obfuscated and may be released to the authentication mechanism in response to successfully decrypted templates and matching biometric samples. A different de-obfuscated password may be released to authenticate the user to a remote or local computer and to encrypt data in transit. This eliminates the need for the user to remember and enter complex passwords on the device.