Disclosed herein is a signature verification apparatus including a first verification circuitry verifying a user's signature by comparing dynamic signature data indicating a change of a user's writing state over time during signing of his or her name by the user and reference data for dynamic signature, a second verification circuitry verifying the user's signature by comparing static signature data indicating a writing path during signing of his or her name by the user and reference data for static signature, and a data registration circuitry registering the reference data for dynamic signature and register the reference data for static signature. In a case where the reference data for static signature has yet to be registered by the data registration circuitry, the second verification circuitry verifies the user's signature by regarding static signature data generated from the reference data for dynamic signature already registered as the reference data for static signature.

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; remove a subset of words from the digital image identified via OCR; determine a plurality of regions of connected markings that remain in the digital image; based at least in part on a pixel density or proximity to an anchor substring of each region, determine that a region contains a 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.

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).

Methods and systems are provided for implementing training of learning models, including obtaining a pre-trained weight set for a learning model on a sample dataset and on a first loss function; selecting at least two tasks having heterogeneous features to be computed by a reference model; obtaining a reference dataset for the at least two tasks; designating a second loss function for feature embedding between the heterogeneous features of the at least two tasks; training the learning model on the first loss function and training the reference model on the second loss function, in turn; and updating the weight set based on a feature embedding learned by the learning model and a feature embedding learned by the reference model, in turn. Methods and systems of the present disclosure may alleviate computational overhead incurred by executing the learning model and loading different weight sets at a central network of the model.

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; remove a subset of words from the digital image identified via OCR; determine a plurality of regions of connected markings that remain in the digital image; based at least in part on a pixel density or proximity to an anchor substring of each region, determine that a region contains a 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.

There is provided an encoding and decoding method and an information recognition device using the same. A code block includes a center coding region and a peripheral coding region arranged around the center coding region. The encoding and decoding method uses the feature of at least one microdot included in the center coding region as codes. The encoding and decoding method uses the feature of at least one microdot included in the peripheral coding region as codes. The encoding and decoding method uses the relative feature between the center coding region and the peripheral coding region as codes. The information recognition device compares the read feature with pre-stored features to decode information such as position codes, object codes, parameter codes and control codes.

Handwriting verification methods and related computer systems, and handwriting-based user authentication methods and related computer systems are disclosed. A handwriting verification method comprises obtaining a handwriting test sample containing a plurality of available parameters, extracting geometric parameters, deriving geometric features comprising an x-position value and a y-position value for each of a plurality of feature points in the test sample, performing feature matching between geometric features of the test sample and a reference sample, determining a handwriting verification result based at least in part on the feature matching, and outputting the handwriting verification result. Techniques and tools for generating and preserving electronic handwriting data also are disclosed. Raw handwriting data is converted to a streamed format that preserves the original content of the raw handwriting data. Techniques and tools for inserting electronic handwriting data into a digital image also are disclosed.

Systems, apparatuses, methods, and computer program products are disclosed for authenticating handwriting on paper-based documents. An example method includes receiving, by an embedded chip device, signature information from a signature device in communication with the embedded chip device. The example method further includes transmitting, by the embedded chip device, document identification information to the signature device. The example method further includes receiving, by the embedded chip device, authentication information from the signature device. Subsequently, the example method includes storing, by the embedded chip device, the signature information and the authentication information as signature authentication metadata in association with the document identification information.

A handwriting input apparatus that displays stroke data handwritten based on a position of an input unit contacting a touch panel, includes circuitry configured to implement a handwriting recognition control unit for recognizing stroke data and converting the stroke data into text data, and an authentication control unit for authenticating a user based on the stroke data, and a display unit for displaying a display component for receiving a signature together with the text data when the authentication control unit determines that a user has been successfully authenticated.

There is provided an encoding and decoding method and an information recognition device using the same. A code block includes a center coding region and a peripheral coding region arranged around the center coding region. The encoding and decoding method uses the feature of at least one microdot included in the center coding region as codes. The encoding and decoding method uses the feature of at least one microdot included in the peripheral coding region as codes. The encoding and decoding method uses the relative feature between the center coding region and the peripheral coding region as codes. The information recognition device compares the read feature with pre-stored features to decode information such as position codes, object codes, parameter codes and control codes.