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).

A method for enhancing efficiency of a computerized electronic signature system by facilitating collection of signatures from multiple signatories is described. The method may initially involve verifying an identity of a first signatory by receiving data from the first signatory electronically, providing a document to the first signatory, receiving a first electronic signature on the document from the first signatory, and receiving a selection from the first signatory of a second signatory to sign the document as a witness. The method may then involve verifying an identity of the second signatory by receiving data from the second signatory electronically, providing the document to the second signatory, receiving a second electronic signature from the second signatory, providing a completed document, including the first signature and the second signature, and notifying the first signatory and the second signatory that the completed document is completed.

A writing pen, a signature recognition method, and a signature recognition system are provided. A non-writing end of the writing pen is equipped with a motion sensing device. The writing pen records a motion trajectory of the non-writing end when the writing pen writes on a plane writing trajectory generating device. When the motion trajectory and/or a plane writing trajectory pass a comparison, a signature comparison device determines that a signature is from a specific person.

A method and a system for fingerprinting signatures and enhanced signature capturing for charge card transactions on mobile communication devices identify and transform a signature into a normalized representation such as a bytestream including coordinate data and combine the normalized representation with some transaction information into combined data. The combined data is further transformed into fixed length data stored for further analyses or uses. Upon the receipt of a dispute, the stored representation(s) of the signature and some of the stored transaction information of the transaction in dispute may be transformed into a first fixed length data. The first fixed length data may be further compared with the stored fixed length data obtained at the time of the transaction to resolve the dispute. The representations of signatures of multiple transactions may be used to determine whether these multiple transactions belong to the same category or to different categories.

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.

Systems and methods described herein discuss securing user-entered data in-transit between a first device and a second device. A user may enter text in a document. A first device may analyze the document to identify the user-entered text. The user-entered text may be separated from the document and transformed into an image using a machine learning algorithm. Transforming the text into an image may secure the data in-transit from the first device to a second device. The second device may receive the image and the document from the first device. The second device may reconstruct the user-entered text from the received image and re-assemble the document from the received document and the reconstructed user-entered text.

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).

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.