A method comprising using at least one hardware processor to: receiving a new incoming check image associated with an account; extracting from the incoming check its features, wherein the features are associated with a plurality of detectors; comparing the features with corresponding features associated with profile check images stored in a CIR; developing a fraud score based on the comparisons, for each of the plurality of detectors; displaying via a user interface, the new incoming check image with at least one highlighted feature, based on business rules, along with confidence indicators for the at least one highlighted feature that illustrate the risk, based on the associated fraud score, for the at least one highlighted feature; and providing, via the user interface, inputs that allow a reviewer to quickly approve or decline the new incoming check image.

A method of operation of a computer for managing a plurality of pieces of drawing data representing a set of components, includes: determining whether target drawing data included in the plurality of pieces of drawing data is related to existing drawing data that has been already stored; acquiring record information when the record information associated with the existing drawing data is stored in a case in which a result of the determining is affirmative; and transmitting record display information for displaying the record information to a terminal. The determining includes determining whether the existing drawing data represents a component having an attribute identical with an attribute of a component represented by the target drawing data. The attribute represents a material, a surface treatment, a processing method, or a combination of two or more of them.

The AUTOMATIC REMOTE DEPOSIT IMAGE PREPARATION APPARATUSES, METHODS AND SYSTEMS (hereinafter MultiCrop) transforms uploaded check images and check deposit information inputs via MultiCrop components image into deposit confirmation outputs. For example, in one embodiment, a user may employ a personal computer connected to a web camera, and/or a smartphone with a built-in camera to initiate the remote deposit by holding a check in front of the camera, and the MultiCrop may capture images of the check and send them to a financial institution for deposit processing.

Provided is an information processing apparatus that extracts one or more candidate character strings to be a candidate of an item value corresponding to a predetermined item, from among multiple character strings included in a document image to be processed. The information processing apparatus first obtains information indicating an attribute group of a preset extraction target, then identifies an attribute group to which each of the multiple character strings belongs from among predetermined multiple attribute groups based on a feature vector corresponding to each of the multiple character strings included in the document image to be processed, and subsequently extracts the character string whose identified attribute group is the same as the attribute group of the extraction target from among the multiple character strings included in the document image to be processed, as the candidate character string.

A character segmentation method and apparatus, and a computer-readable storage medium are provided. The method includes converting a character area image into a grayscale image; converting the grayscale image into an edge binary image by edge detection; acquiring character box segmentation blocks from the edge binary image by projection; and determining a target character area from the character box segmentation blocks by contour detection, and performing character segmentation on the character area image according to the target character area; or comprises: converting a character area image into a grayscale image; performing clustering analysis on the grayscale image by fuzzy C-means clustering, and binarizing the grayscale image according to the analysis result; acquiring character positioning blocks from a binary image by projection; and performing character segmentation on the character area image according to position information of the character positioning blocks. Character segmentation can be performed on a relatively low quality image.

A data processing method may include one or more of: obtaining, from an obtained floor plan image, at least one guide line and an actually-measured length corresponding to each of the at least one guide line, splitting the floor plan image into at least one section, based on a line extending from a scale of the at least one guide line, determining, as an adjustable space, a section in which both actually-measured lengths respectively corresponding to a width and a height exist, among the at least one section, adjusting a ratio of a greatest horizontal length and a greatest vertical length of the floor plan image, according to a ratio of a greatest actually-measured length corresponding to a width of the floor plan image and a greatest actually-measured length corresponding to a height of the floor plan image, and adjusting a horizontal length and a vertical length of the adjustable space according to ratios of the greatest actually-measured lengths respectively corresponding to the width and height of the floor plan image and actually-measured lengths respectively corresponding to a width and a height of the adjustable space.

A system and method for extracting data from a received fax from a medical provider and matching it with a corresponding preauthorization record using modeling techniques. A received fax is converted into text via OCR, relevant keys are extracted from the text using modeling techniques and differential probabilities are calculated for each key that are then compared to the candidate preauthorization records using logistic regression models to find the most probable matching candidate records. Candidate record matches are ranked by matching probability and the highest ranked candidate record is considered the matching record to the received fax.

Output can be provided from a selected biometric analysis task that is one of a plurality of biometric analysis tasks based on an image provided from an image sensor. The selected biometric analysis task can be performed in a deep neural network that includes a common feature extraction neural network, a plurality of biometric task-specific neural networks and a plurality of expert pooling neural networks that perform the plurality of biometric analysis tasks by inputting the image to the common feature extraction network to determine latent variables. The latent variables can be input to the plurality of biometric task-specific neural networks to determine a plurality of first outputs. Concatenated first output results can be formed and the concatenated plurality of first result outputs and the latent variables can be input to the plurality of expert pooling neural networks to determine one or more biometric analysis task outputs.

The system receives an indication of a sensitive operation. The system obtains a unique ID of a user's UE. Based on the unique ID of the UE, the system retrieves a visual authentication method including a visual ID. The system records the visual ID, and retrieves a corresponding stored visual ID. The system performs a liveness check associated with the visual ID, to determine whether the visual ID is a recording or a live version of the visual ID. Upon determining that the visual ID is the recording, the system refuses to authenticate the user. Upon determining that the visual ID is the live version of the visual ID, the system compares the visual ID and the corresponding stored visual ID to determine whether the visual ID and the corresponding stored visual ID match. Upon determining that the visual ID and the corresponding stored visual ID match, the system authenticates the user.

A system and method for extracting data from a received fax from a medical provider and matching it with a corresponding preauthorization record using modeling techniques. A received fax is converted into text via OCR, relevant keys are extracted from the text using modeling techniques and differential probabilities are calculated for each key that are then compared to the candidate preauthorization records using logistic regression models to find the most probable matching candidate records. Candidate record matches are ranked by matching probability and the highest ranked candidate record is considered the matching record to the received fax.