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 extracting signatures and assigning signatory name attributions to the extracted signature images from unstructured digital documents is provided. The method includes: receiving a document that includes a signature; detecting a first region within the document in which the signature is present; applying a region-based convolutional neural network to the detected first region in order to demarcate a boundary that surrounds the detected first region; detecting a second region within the document in which a name that relates to the signature is present, by scanning the document to obtain a set of text words and then applying a named entity recognition (NER) machine learning algorithm to determine which text words are names; and assigning a signatory name attribution to the name that is present in the second region, based on a calculated distance between the name and the demarcated boundary.

A system for election document processing is disclosed. Image data representative of a scanned election document is matched with a template for processing and consideration of a new elections record. The template defines areas of interest and rules for processing the image data. Data associated with picture elements defining the areas of interest is applied to predetermined validation functions to validate whether the image data includes sufficient information for populating predefined fields and accepting the scanned election document. Additional functions are applied to the image data to compare the image data with information of a voter registration database in order to verify a voter associated with the election document. The scanned election document is further processed for validation sampling.

The present disclosure allows digitizing handwritten signatures efficiently. A live stream of image frames is received from a digital camera unit of an electronic device. The received image frames are displayed and a guideline pattern defining a first target area and a second target area is overlaid. The content of a first image frame section of a current frame overlaid by the first target area is read. A multidimensional machine-readable code decoder is applied to the read content in order to interpret the read content. If the read content is successfully interpreted and if a precondition is met, a second image frame section of the current image frame overlaid by the second target area is captured.

A classifier receives a document and analyzes the document to determine one or more predicted roles of one or more signatories, each predicted role determined based on one or more signature elements in the content of the document executed by the one or more signatories. The classifier evaluates each of the one or more predicted roles in view of a plurality of expected signatory role characteristics of a plurality of categories of documents of a transaction to select a particular category associated with the document from among the plurality of categories. The classifier classifies the document within the transaction as a particular logical type identified by the particular category from among a plurality of logical types for the transaction.

The present invention relates to a method, system and computer program product for defect enhancement. According to the method, a plurality of proposed regions from a plurality of images taken for a display panel is obtained. Each of proposed region of the plurality of proposed regions include a suspected defect on the display panel. At least two proposed regions from the plurality of proposed regions that deserve to be superimposed based on a set of conditions is determined. The at least two proposed regions for acquiring an enhanced defect are superimposed.

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.

A device receives information indicating first names and last names of individuals and applies different cursive fonts to each of the first names and the last names to generate images of different cursive first names and different cursive last names. The device applies different transformations to the images of the different cursive first names and the different cursive last names to generate a set of first name images and a set of last name images. The device combines each first name image with each last name image to form a set of signature images and trains a neural network model, with the set of signature images, to generate a trained neural network model. The device receives an image of a signature and processes the image of the signature, with the trained neural network model, to recognize a first name and a last name in the signature.

The method, system, and non-transitory computer-readable medium embodiments described herein are directed to verifying documents. In various embodiments, a server may receive a first image of a front-side of a document. The server may extract a first feature of the front-side of the document from the first image and identify a first environmental feature from the first image. The server may receive a second image of a backside of the document and identify a second feature of the backside of the document from the second image. The server may also identify a second environmental feature from the second image. The server may verify the document by confirming that the first feature matches the second feature and the first environmental feature matches the second environmental feature.

A communication system that provides wireless service to at least one wireless device is provided. The communication system includes a baseband controller communicatively coupled to a plurality of radio points and at least one image capture device at a site. The baseband controller is configured to determine a signature vector for a wireless device associated with a first user. The communication system also includes a machine learning computing system configured to determine an image-based location of the first user based on image data from the at least one image capture device. The communication system is also configured to determine mapping data that associates the signature vector with the image-based location of the first user.