One embodiment provides a method, including: receiving, at an electronic device, a handwriting input operation by a user; acquiring, using a processor, a first input feature information associated with the handwriting input operation; and generating, using a processor, an index based on the first input feature information. Other aspects are described and claimed.

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. The geometric features may further comprise values derived from the geometric parameters, such as direction and curvature values. The handwriting verification result can be further based on a count of unlinked feature points. Handwriting-based user authentication methods can employ such handwriting verification methods, or other handwriting verification methods.

A method and system for the spotting of keywords in a handwritten document, the method comprising the steps of inputting an image of the handwritten document, performing word segmentation on the image to obtain segmented words, performing word matching, and outputting the spotted keywords. The word matching itself consisting in the substeps of performing character segmentation on the segmented words, performing character recognition on the segmented characters, performing distance computations on the recognized characters using a Generalized Hidden Markov Model with ergodic topology to identify words based on character models and performing nonkeyword rejection using a classifier based on a combination of Gaussian Mixture Models, Hidden Markov Models and Support Vector Machines.

Remote deposit of checks can be facilitated by a financial institution. A customer's general purpose computer and image capture device may be leveraged to capture an image of a check and deliver the image to financial institution electronics. Additional data for the transaction may be collected as necessary. The transaction can be automatically accomplished utilizing the images and data thus acquired.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving an enrollment biometric identifier of a user. Determining a matching threshold based on a characteristic of the enrollment biometric identifier of the user. Storing the matching threshold in an enrollment profile for the use in association with the enrollment biometric identifier.

A method implemented on a visual computing device to authenticate one or more users includes receiving a first three-dimensional pattern from a user. The first three-dimensional pattern is sent to a server computer. At a time of user authentication, a second three-dimensional pattern is received from the user. The second three-dimensional pattern is sent to the server computer. An indication is received from the server computer as to whether the first three-dimensional pattern matches the second three-dimensional pattern within a margin of error. When the first three-dimensional pattern matches the second three-dimensional pattern within the margin of error, the user is authenticated at the server computer. When the first three-dimensional pattern does not match the second three-dimensional pattern within the margin of error, user is prevented from being authenticated at the server computer.

A method, system and device are provided for authenticating a signature made by a signer. When the signer is making the signature a biometric quantity associated with the signer may be sampled. The sampling generates a sequence of biometric samples of the biometric quantity. Continuity of generated sequence of biometric samples of the biometric quantity is validated. Each of the biometric samples of the biometric quantity in the generated sequence is validated. The signature of the signer may be authenticated when the generated sequence of biometric samples is validated, and a continuity of the biometric samples is validated.

A method for rendering visible security information of a digitally stored image which has a plurality of pixels and at least one piece of digitally coded information comprises at least the following steps: allocating at least some of the pixels to at least one image region; determining an average color value of the pixels allocated to the image region; determining two mutually complementary color values whose average color value is at least approximately identical to the determined average color value of the pixels allocated to the image region; depicting the image region and at least part of the digitally coded security information in a visually perceptible manner. The depicted image region thereby has a first of the mutually complementary color values. The part of the digitally coded security information is depicted within the image region and has a second of the mutually complementary color values.

Systems and associated methodology are presented for Arabic handwriting synthesis including accessing character shape images of an alphabet, determining a connection point location between two or more character shapes based on a calculated right edge position and a calculated left edge position of the character shape images, extracting character features that describe language attributes and width attributes of characters of the character shape images, the language attributes including character Kashida attributes, and generating images of cursive text based on the character Kashida attribues and the width attribues.

A method includes storing a research paper and a set of candidate resources including media content. The method further includes encoding each of one or more first content fields in the research paper into a first vector based on a first field type associated with each of the one or more first content fields. The method further includes encoding each of one or more second content fields in each of the parsed set of candidate resources into a second vector, based on a second field type associated with each of the one or more second content fields. The method further includes comparing the first vector with the second vector to determine a final set of resources based on the comparison. The method further includes controlling a display screen to output the determined final set of resources and the research paper.