Certain aspects of the present methods and systems may focus on computer implemented methods of obtaining digitized hand-writing data corresponding to a sample of a needed code point of a set of code points. Such methods may include: obtaining a sample of digitized handwritten text, the sample of digitized handwritten text including glyph data corresponding to a first glyph, the first glyph corresponding to the needed code point of the set of code points; associating the first glyph with the needed code point; identifying stroke data in the glyph data, the stroke data corresponding to a stroke component of the first glyph, determining a plurality of dimensional values of the stroke component in the stroke data; and associating the plurality of dimensional values with a new code point sample of the needed code point in a code point set data structure.

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 projection and measurement system (100) comprising a projection unit (110), a tracking unit (120), and a control unit (130). The control unit (130) is configured to control the projection unit to determine a feature of the body part of the user, generate an optical guide to be projected onto the body part based on the determined feature, and control the projection unit to project the optical guide. The control unit (130) is further configured to control the projection unit (110) to project the optical guide onto the body part in an asynchronous manner with respect to the detection of the image of the body part, and/or to generate a modulated form of the optical guide and/or illuminating light to be projected onto the body part.

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.

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 marking analysis system includes a marking data storage unit to store a plurality of marking data indicating a plurality of positions marked by a user in a book so as to correspond respectively to a plurality of users, a marking distribution analysis unit that analyzes the marking data and calculates a marking frequency for each of a plurality of unit areas in the book, and generates marking distribution characteristic data indicating a distribution of the marking frequency with respect to a position in the unit area, a marking distribution characteristic data storage unit to store the marking distribution characteristic data, and a similar user retrieval unit that, when determining that the distribution of the marking frequency indicated by the marking distribution characteristic data of a target user selected as a processing target and the distribution of the marking frequency indicated by the marking distribution characteristic data of another user are similar, extracts the another user as a similar user who is similar to the target user.

Methods, systems and processor-readable media for vehicle classification. In general, one or more vehicles can be scanned utilizing a laser scanner to compile data indicative of an optical profile of the vehicle(s). The optical profile associated with the vehicle(s) is then pre-processed. Particular features are extracted from the optical profile following pre-processing of the optical profile. The vehicle(s) can be then classified based on the particular features extracted from the optical feature. A segmented laser profile is treated as an image and profile features that integrate the signal in one of the two directions of the image and Fisher vectors which aggregate statistics of local patches of the image are computed and utilized as part of the extraction and classification process.

Biometric data are obtained from a biometric sensor 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 a locally stored, encrypted template. For extra security, the biometric matching may be enhanced by the use of an entered PIN. The biometric template and other sensitive data are encrypted using hardware elements of the computing device and ASIC, together with a PIN hash. A stored obfuscated Password is de-obfuscated and may be released to the authentication mechanism in response to a successfully decrypted template and matching biometric sample. A different de-obfuscated password may be released to authenticate the user to a remote computer and to encrypt data in transit. This eliminates the need for the user to remember and enter complex passwords on the device.

A handwriting geometry recognition and calibration system by using neural network and mathematical feature includes: a pre-processor for pre-processing coordinate points of geometric figures from user's handwriting so as to get a plurality of sample points which expresses the geometric figures to be recognized; a neural network connected to the pre-processor for receiving the sample points of the geometric figure and recognizing the geometric figure so as to acquire a coarse class of the geometric figure; and an mathematical logic unit connected to the neural network for receiving recognition results from the neural network, including coarse classifications which are used in a secondary classification by using conventional mathematical recognition logics so as to determine an exact geometry shape of the geometric figure; then the geometric figure being calibrated so as to get a geometry with a regular shape.

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.