82results about "Signature reading/verifying" patented technology

A system and a method for verifying authenticity of a physical copy and a digital copy of a document are disclosed. The method comprises registering a document in a repository by storing details related to the document in a location of the repository. A symbology for the document is generated. The symbology is an identifier of the location of the repository comprising the document. The symbology is associated with either a physical or a digital copy of the document. The digital copy of the document is printed to generate a printed copy. The printed copy or the physical copy of the document is scanned to generate a scanned image. The document and the details related to the document present at the location of the repository are accessed. The scanned image is compared with the document stored in the repository to determine the authenticity of either the physical copy or the digital copy of the document.

The invention relates to the technical field of information security, and discloses a handwriting dynamic encryption and electronic signature method based on a national secret algorithm, and the method comprises the steps: obtaining the handwriting data of a user, and carrying out the preprocessing of the data to remove noise and errors; carrying out feature extraction on the preprocessed handwriting data, extracting corresponding track features, speed features and pressure features, and generating a feature hash value of a unique identifier through a cryptographic SM3 hash algorithm; splicing and combining the feature hash value and a user private key, deriving a dynamic key through a national secret SM2 algorithm, and generating an electronic signature by using the dynamic key; an electronic signature is combined with a timestamp to form a structured signature packet, and the signature packet is hierarchically encrypted and stored in a database through a national cryptographic SM4 algorithm. According to the method and the system, the handwritten signature picture, the digital signature technology, the electronic signature technology and the biological recognition and layout file processing technology are fused, so that the safety and the reliability of electronic document signing are improved.

Aspects of the embodiments described herein are related to systems, methods, and computer products for performing automatic check fraud detection. Aspects of embodiments described herein provide artificial intelligence systems and methods that analyze an image of an incoming check, compare the features of the image to the associated features on a reference check, and generate a check fraud score based on the comparison, and either approve the incoming check or flag the incoming check for manual review.

The invention provides a character recognition management method and system fusing multi-modal data, and relates to the technical field of data processing, and the method comprises the steps: continuously capturing heterogeneous multi-source unsteady business instruction sequences submitted by employees; based on the heterogeneous multi-source unsteady service instruction sequence, executing a tensor reconstruction operation of an embedded space through a cross-modal depth feature alignment network, and outputting a structured high-dimensional tensor of a dimension specification; and based on the structured high-dimensional tensor, performing homogenized set division by using a K-means clustering algorithm driven by feature similarity to generate a feature set cluster with a compact feature. According to the invention, efficient management of text information in a banking business scene is realized.

The application discloses a kind of doctoral and master degree thesis authorization page identification method, comprising: extracting electronic version degree thesis by page to form image set to be examined;According to the image set to be examined, paper version degree thesis is scanned to generate image set to be examined;Image set to be examined is sent into authorization page identification classifier, it is judged whether there is authorization page;If not, output the conclusion without authorization page, if there is, then execute next step;Handwritten signature of author or tutor is detected in authorization page;Output authorization page automatic detection result.The application converts effective authorization page into image classification and target detection problem by automatic detection, and realizes the automatic detection of authorization page and the automatic discrimination of whether authorization page is effective by using image classification and target detection technology based on deep convolution network.

The invention provides a non-contact double-recording signature method and device based on end intelligence, and the method comprises the steps: loading a face recognition model and a gesture recognition model preset in end side equipment, and initializing a real-time video stream of a camera; face detection, living body judgment and facial feature comparison are carried out based on the real-time video stream, and an identity verification result is generated; synchronously recognizing a gesture of a user or an object moving track, matching a standard signature template through dynamic time warping, and generating a track consistency verification result; the identity verification result, the track consistency verification result and the signature track data are locally encrypted and stored, and are asynchronously uploaded to a server when the network is recovered; and through a timestamp alignment mechanism, audio and video streams in a signature process, an identity verification result and track data are synchronously recorded, encrypted and stored. According to the invention, the authenticity and security of signature are effectively improved, the risk of data leakage is reduced, and the system stability is enhanced.

The invention provides a title review process management method and system, and relates to the technical field of review process management, and the method comprises the steps: constructing a database for storing historical review features, obtaining to-be-reviewed data, extracting the features of the to-be-reviewed data to obtain to-be-reviewed features, and carrying out the preliminary review through a preset review rule to obtain a preliminary review result; if the preliminary review result conforms to the expectation, calculating a first similarity between the to-be-reviewed feature and a working class sub-feature in the historical review feature, and if the preliminary review result does not conform to the expectation, sending out a material doubt signal; and judging whether the first similarity is greater than a first preset similarity threshold and less than 1, if so, sending a material doubt signal, and otherwise, sending a material legal signal. The review efficiency can be improved.

A mail signature verification system employs a software that is configured to remove pixels of a digital photograph taken of a signature through an opaque security window. The mail piece can be signed and then the signature is covered with the signature cover having a security window therein. The signature reading station of a mail processing machine compresses the security window against a press plate and illuminates the signature with a light element while taking the digital photograph. The software then analyzes the digital photograph to remove pixels having a grey scale value below an upper grey scale threshold and remove pixels above a lower grey scale threshold, thereby rendering a signature with these darkest of pixels over the lightest of pixels.

The application relates to the technical field of image matching, in particular to a handwritten data double-track recording system and recording equipment capable of preventing tampering and easy tracing, which comprises an image acquisition module, a paper handwriting extraction module, a key point evaluation module and a handwriting security evaluation module.

According to the inspection and detection qualification intelligent approval method based on multi-modal semantic understanding and block chain evidence storage provided by the invention, a four-in-one technical architecture of semantic understanding, credible consensus, intelligent decision making and judicial evidence storage is constructed; through four key steps of dynamic knowledge graph construction, multi-modal material analysis, cross-chain multi-node collaborative approval and whole-process block chain evidence storage, intelligent and reliable inspection and detection qualification approval can be realized.

The invention relates to an electronic certificate file detection method, and belongs to the field of data processing. Comprising the following steps: configuring a certificate file detection standard; rapidly detecting the OFD format; extracting OFD file information, and constructing an information association relationship: analyzing the license file based on a standard, realizing automatic extraction of an electronic license OFD file directory structure and an XML node relationship, and constructing an OFD analysis engine; oFD file information detection: carrying out OFD file information verification based on a certificate file detection standard configured by a user, an OFD file information extraction result and certificate information in an electronic certificate library; oFD file layout detection: detecting the layout of the certificate file based on a certificate file detection standard configured by a user and an OFD file information extraction result; detecting signature information; and visual feedback is realized. The OFD file internal data verification blank is filled, an OFD format detection framework is constructed, the tampering recognition rate is high, and the efficiency is high.

Disclosed embodiments can pertain to utilizing biometric behavior to prevent inappropriate entry of sensitive information. Current biometric behavior of a user, specific to the user's interaction with an electronic form, can be acquired and compared to benchmark biometric behavior of the user associated with past interaction with the electronic form to create a comparison result. A similarity score based on the comparison result can then be established. It can be inferred that sensitive information was incorrectly entered into the electronic form when the similarity score satisfies a predetermined similarity threshold. The sensitive information can be identified, and a machine learning model can be triggered to generate a risk score based on the similarity score and the identified sensitive information. A user can be prompted to redact incorrectly entered sensitive information when the risk score satisfies a predetermined risk threshold.

Various embodiments herein each include at least one of systems, methods, and software for identity verification. Some such embodiments identify a user based on analysis of their handwriting. Some such embodiments may be utilized to authenticate an individual, for emergency authentication or when other authentication solutions are not available, as one of a two or more step authentication process, authenticating checks, authenticating a signature or other handwriting on another document, and the like. One embodiment, in the form of a method, includes storing a set of handwriting characteristics of an individual identified during processing of a plurality of handwriting samples of the individual. The method may then determine whether an input handwriting sample is handwriting of the individual based on the stored set of handwriting characteristics of the individual and then output a result of the determining.

The invention discloses a legal document fall positioning and quality inspection method and device based on semantic understanding, and relates to the technical field of legal review, and the method comprises the steps: analyzing electronic document data, extracting text content, associating the text content with corresponding coordinate metadata, and generating multi-modal document data; performing deep semantic and structural analysis on the multi-modal document data, extracting a text sequence, executing natural language analysis and semantic analysis, obtaining a phrase role labeling result, judging a termination position, and obtaining a semantic boundary; based on the semantic boundary and the coordinate metadata, performing retrieval positioning on the multi-modal document data, extracting coordinate text data and page evidence image segments, obtaining a structured entity and segmented signature blocks through entity recognition and signature detection, and constructing a signature element map; according to the method, automatic verification and accurate fund positioning of the document are realized, the processing efficiency is improved, manual intervention is reduced, and the method is widely applicable to automatic examination and compliance inspection of the electronic document.

The application provides an electronic signature method, system, device and storage medium based on multiple verifications, and the method comprises the following steps: a mobile terminal receives a request of a user for generating an electronic signature; electronic signature currently signed by the user and signature related information in the signing process are collected; storage address information is generated according to at least the pattern information of the current electronic signature and the signature related information; and at least the pattern information and the storage address information are uploaded to a block chain node. The application can confirm that the signatory is the person himself through multiple verifications, and can protect the signature information from being lost and being tampered with through the block chain technology, thereby improving the authentication security.

The invention provides a multi-modal handwritten signature verification method based on global features, which comprises the following global feature extraction steps of a multi-modal handwritten signature: S1, collecting multiple modal data of a handwritten electronic signature of a user, including a signature image pic and a signature time sequence seq; wherein the time sequence at least comprises three physical characteristics (i = 1, 2, 3,..., T) of an abscissa sequence {xi}, an ordinate sequence {yi} and a pressure sequence {pi}, and T is total time; s2, extracting global features from the signature image and the time sequence at the same time, and splicing the global features to form a handwritten electronic signature feature vector; for the signature image, the obtained features comprise a signature area A, a height H = ymax-ymin, a width W = xmax-xmin, an aspect ratio H / W, a gravity center Cx = Sigma ixipi / Sigma ixi, and Cy = Sigma iyipi / Sigma iyi.

The embodiment of the application provides an electronic signature method, an electronic signature device, an electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: obtaining a pre-stored name text of a target object; performing text recognition on the pre-stored name text to obtain a total number of words of the pre-stored name text; obtaining a word number threshold value that can be displayed on a first signature page; if the total number of words is greater than the word number threshold value, performing pagination on the first signature page to obtain a plurality of second signature pages; obtaining an initial electronic signature of the target object on the second signature page; and arranging the initial electronic signatures of all the second signature pages to obtain a target electronic signature. According to the embodiment of the application, the total number of words of the pre-stored name text is used to distinguish the signature scene, and the accuracy of electronic signature recognition can be improved.

The application discloses an OFD file intelligent abstract generation method fusing OCR and deep learning, relates to the technical field of electronic file processing and management, and preliminarily scans text and tables through image recognition technology to identify fuzzy areas; for the areas containing handwriting, image enhancement is combined with semantic analysis to restore the clarity; then, seal and signature key elements are extracted, layout analysis is used to divide the document area and judge the relevance; if the preset condition is met, the information is integrated through a computer vision model to determine the overall logical structure; then, a machine learning model is applied to analyze the semantic relationship to form a structured information set; finally, a concise abstract containing the writing date and the year is generated by fusing the visual model. The application ensures efficient extraction and structured presentation of complex document information, and significantly improves the accuracy and practicability of document processing.

A method can include identifying handwritten elements from a document image. The method can include generating embeddings for the handwritten elements, and the embeddings may represent features extracted from the handwritten elements. The method can include clustering the embeddings into clusters. The method can include processing the document image to identify areas within the document image designated for placement of a signature. The method can include, based upon locations of the areas within the document image, tagging each cluster as a signature cluster or as a non-signature cluster. The method can include outputting indications that a handwritten element is a signature element or is a non-signature element.

Embodiments of the present application provide a handwritten signature recognition method and device, electronic equipment and storage medium. The handwritten signature recognition method comprises: obtaining a handwritten signature picture to be recognized; performing single character detection on the handwritten signature picture to obtain position information of a target character region; merging target character regions that need to be merged based on the position information to obtain a merged character region; recognizing characters corresponding to each character region, the character region including the target character region that has not been merged and the merged character region; comparing the characters with a user name in user information, and determining that a handwritten signature in the handwritten signature picture is valid when the comparison result is consistent. The handwritten signature recognition method can improve the accuracy of handwritten signature recognition.

Disclosed embodiments can pertain to utilizing biometric behavior to prevent inappropriate entry of sensitive information. Current biometric behavior of a user, specific to the user's interaction with an electronic form, can be acquired and compared to benchmark biometric behavior of the user associated with past interaction with the electronic form to create a comparison result. A similarity score based on the comparison result can then be established. It can be inferred that sensitive information was incorrectly entered into the electronic form when the similarity score satisfies a predetermined similarity threshold. The sensitive information can be identified, and a machine learning model can be triggered to generate a risk score based on the similarity score and the identified sensitive information. A user can be prompted to redact incorrectly entered sensitive information when the risk score satisfies a predetermined risk threshold.

The invention provides a handwritten signature detection method and device and an electronic product, and the method comprises the steps: carrying out the joint prediction: predicting an input handwritten signature image through a detection head, outputting the parameters of a predicted rotating rectangular frame and a predicted horizontal rectangular frame, and carrying out the consistency constraint; generating a corresponding Gaussian attention map based on the predicted rotating rectangular frame parameter; carrying out point-by-point multiplication on the Gaussian attention map and an originally input handwritten signature image, and extracting a region of interest; the region of interest is sent to a classification head, a three-classification task is completed, three classification probability values are output, a rotating rectangular frame with the classification probability of the handwritten signature larger than a set threshold value is a detected handwritten signature frame, finally, redundant rotating rectangular frames are removed through maximum suppression, and a detection result is obtained. According to the method and the device, the problem of handwritten signature detection under target inclination, direction variation and non-signature handwritten signature interference in the prior art can be effectively solved.

The application discloses a kind of based on pseudo paragraph handwriting prototype guide handwriting feature extraction method, including collecting multiple handwriting samples from multiple writers to generate handwriting prototype;Construct positive sample set and negative sample set and design prototype contrast learning loss function to obtain more distinctive handwriting prototype;Using feature encoder selects positive sample handwriting features and negative sample handwriting features and designs guide contrast learning loss function to guide the training of feature encoder;Using the trained feature encoder determines the authenticity of handwriting;Two contrast learning loss functions designed by the application can guide the encoder to effectively extract handwriting prototype from pseudo handwriting paragraph and extract discriminative handwriting features, thereby improving the accuracy of handwriting verification.

The application discloses a method for recognizing signing intention based on handwriting features, and the signatures in the active sample library and the passive sample library are mutually exclusive; online acquisition of user's handwritten electronic signature is compared and verified with the corresponding signature in the active intention sample library, if the verification is consistent, the active intention setting is executed, if the verification is inconsistent, further comparison and verification with the signature in the passive intention sample library is performed, if the verification is consistent, the passive intention setting is executed, the pre-warning is started, if the number of inconsistent verification exceeds the predetermined number, the signing is locked. The two kinds of intention samples are consistent in static features such as writing method, but the dynamic features are different, and the handwriting verification algorithm is used for verification, which is mutually exclusive. It is difficult to be imitated, and is a kind of private and safe passive sample method. It is beneficial to protect the personal safety and property safety of the person under duress.

The invention relates to a book label identification method and system based on a dot matrix technology and AI analysis, and the method comprises the steps: carrying out the hierarchical coding of an identity region, a chapter region and an exercise region of a book based on a preset coding rule; when the code information sent by the identification terminal is received, identifying the code information to confirm the identity information of the student to which the book belongs; after identification of the identity information is completed, acquiring correction information of the current chapter exercise area; and when the correction completion information of the current chapter is recognized, the obtained correction information, the identity information and the chapter information are stored in an associated manner. The method has the advantages that a teacher can conveniently correct the book exercise book, and the correction efficiency is improved.

The invention relates to a traffic police command action attitude acquisition method and system and a storage medium, and the method comprises the steps: setting an action capture laboratory which is internally provided with an activity space and an infrared action capture camera; obtaining a motion capture close-fitting garment matched with the collection object, wherein a plurality of target balls are attached to the motion capture close-fitting garment; pre-scanning a collected object through an infrared motion capture camera so as to check the initial position of each target ball; when a collection object makes a command action, three-dimensional position information and timestamp information of each target ball are captured through an infrared motion capture camera so as to be mapped into a human skeleton model, and traffic police command action sequence data including skeleton rotation angles, joint displacement and time sequence association information are generated. Compared with the prior art, the method has the advantages that skeleton movement tracks and posture change details in the traffic police command actions can be precisely restored, high-precision acquisition and restoration of the traffic police command actions are realized, and high-quality traffic police command action data which can be used for analog simulation tests and closed site tests is generated.

The invention discloses a signature identification method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring a to-be-detected image; for each signature in the to-be-detected image, the signature is split into a plurality of stroke units, and the stroke units are minimum units forming the signature; extracting handwriting features of each stroke unit, the handwriting features including geometric features and contour features; a handwriting recognition model is adopted to process and analyze the handwriting features, a plurality of matching probabilities are output, each matching probability corresponds to one target object, and the matching probabilities are used for indicating the probability that the signatures belong to the target objects; and determining the target object corresponding to the maximum matching probability in the plurality of matching probabilities as the affiliation object of the signature in the to-be-detected image. The technical problem of low accuracy of an identification result caused by neglecting detail information of a signature in a handwriting identification method in related technologies is solved.

The invention discloses a multi-dimensional biological characteristic visualization method and system for a signature sequence, and the method comprises the steps: firstly, obtaining online signature time sequence data through recording key point information in an electronic signature writing process; then, performing multi-dimensional feature decoupling calculation on the signature sequence data, and extracting implicit dynamic biological feature vectors; then, defining a visual mapping rule base, and respectively mapping the multi-dimensional biological characteristics into corresponding visual channel parameters; and finally, according to the visual channel parameters, rendering to generate a signature feature image set containing dynamic information of different dimensions. According to the method, through a regularized mapping mechanism, a dynamic pen wielding behavior which is difficult to perceive visually in a signature is converted into a high-resolution static image representation, so that the problem of isomerism of online time sequence data and offline image data in a feature space is effectively solved; and an interpretable multi-channel data basis is provided for signature authenticity identification algorithm research and artificial judicial handwriting identification.