The invention relates to the field of providing a trusted environment for executing an analogue-digital signature. The claimed document-signing device in the form of a stylus includes a protective compartment, in which the following are disposed: a microcontroller with a programme code; a memory with a secret digital signature key; and additionally inertial sensors, which are connected to the microcontroller; a lens; and a camera, which is also connected to the microcontroller. A wireless interface is used in order to communicate with a computer. The inertial sensors serve to verify the handwritten signature of the user, while the lens and camera serve to carry out a comparison with the text of an electronic document uploaded via the wireless interface. In this way it is ensured that verified information enters the trusted environment of the stylus.

Systems and methods to automatically generate classifiers are provided. A labeled dataset is initially received. The dataset may be for a positive class, or may be a negative for a class, or a false positive class. N features that are predictive for the class (or false positive or the negative class) are identified. These features are combined within a classifier dictionary. Medical records received may be processed in order to be machine readable. Features within the medical records are identified and are compared against the dictionary of classifiers. Matches indicate classes within the medical record. The classifier dictionary may be periodically updated in response to insufficient classification accuracy, or when new data becomes available.

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.

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.

Provided are an augmented handwritten signature authentication method and an electronic device supporting the same, the method including: receiving a primary touch input corresponding to a primary handwritten signature input of a user; receiving, after the primary handwritten signature input is received and a specified time period elapses, a secondary mark input of the user; obtaining primary touch position data corresponding to the primary touch input, and the secondary mark input; and performing user authentication by comparing the primary touch position data corresponding to the primary touch input and secondary touch position data related to at least part of the secondary mark input with prestored primary touch registration data and secondary touch registration data, respectively.

Systems and methods to automatically generate classifiers are provided. A labeled dataset is initially received. The dataset may be for a positive class, or may be a negative for a class, or a false positive class. N features that are predictive for the class (or false positive or the negative class) are identified. These features are combined within a classifier dictionary. Medical records received may be processed in order to be machine readable. Features within the medical records are identified and are compared against the dictionary of classifiers. Matches indicate classes within the medical record. The classifier dictionary may be periodically updated in response to insufficient classification accuracy, or when new data becomes available.

Systems and methods to automatically generate classifiers are provided. A labeled dataset is initially received. The dataset may be for a positive class, or may be a negative for a class, or a false positive class. N features that are predictive for the class (or false positive or the negative class) are identified. These features are combined within a classifier dictionary. Medical records received may be processed in order to be machine readable. Features within the medical records are identified and are compared against the dictionary of classifiers. Matches indicate classes within the medical record. The classifier dictionary may be periodically updated in response to insufficient classification accuracy, or when new data becomes available.

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.

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.

The signature input device includes a display unit, a guide display processing unit, an input unit, a write processing unit, and an output processing unit. The display unit includes a screen displaying an image and has an input field indicating an area at which a signature is written. The guide display processing unit displays guide information when a signature is written inside the input field. The input unit is provided on a surface of the display unit and receives an input by being in contact with a detection object. The write processing unit displays, inside the input field, a locus of the detection object brought into contact with the input field. The output processing unit outputs data of an image displayed inside the input field.