A method for performing automatic license plate recognition includes: performing character detection on a to-be-processed image; when a number of character objects constituting characters of a license plate are detected, obtaining a representative point for each character object; calculating a pattern line with respect to the character objects; calculating a skew angle defined by the pattern line and a preset reference line; rotating each character object based on the skew angle to obtain an adjusted image; and performing character recognition on the adjusted image to obtain a recognized character for each character object.

There is provided a method and a system configured to compensate for image distortions.

An image analysis apparatus rectifies a first image and a second image to generate a first rectified image and a second rectified image respectively. A first container door in the first rectified image and a second container door in the second rectified image have the same sizes and the same pixel coordinates. The image analysis apparatus divides the first rectified image into a plurality of first blocks and divides the second rectified image into a plurality of second blocks in a same manner. The image analysis apparatus calculates respective correlations between every first block and its corresponding second block. Each of the correlations includes a grayscale correlation and at least one color correlation. The image analysis apparatus determines whether the first container door and the second container door represent the same container door based on the correlations.

Systems and methods for mobile enrollment in automated clearing house (ACH) transactions using mobile-captured images of financial documents are provided. Applications running on a mobile device provide for the capture and processing of images of documents needed for enrollment in an ACH transaction, such as a blank check, remittance statement and driver's license. Data from the mobile-captured images that is needed for enrolling in ACH transactions is extracted from the processed images, such as a user's name, address, bank account number and bank routing number. The user can edit the extracted data, select the type of document that is being captured, authorize the creation of an ACH transaction and select an originator of the ACH transaction. The extracted data and originator information is transmitted to a remote server along with the user's authorization so the ACH transaction can be setup between the originator's and receiver's bank accounts.

Systems and methods provide a deployable cloud-agnostic redaction container for performing optical character recognition and redacting information from a document using a cloud-based, guided redaction framework. An example method for document redaction includes receiving a plurality of documents and extracting pages from the plurality of documents. The method then determines, based on a load balancing criterion, a processing order for the pages extracted from the plurality of documents, and performs, based on the processing order, an optical character recognition process and a redaction process on the pages to generate redacted pages. The redacted pages are provided for transmission or storage to a cloud data management platform.

A method for categorizing and adjusting an orientation of a clinical image is implemented using a system that includes a categorization module and a plurality of orientation modules. The method includes: in response to receipt of the clinical image, performing, by the categorization module, a categorization operation so as to categorize the clinical image into one of a plurality of predetermined categories; transmitting the clinical image to a corresponding one of the plurality of orientation modules based on a result of the categorization operation; and performing, by the corresponding one of the plurality of orientation modules, an orientation adjusting operation for adjusting the orientation of the clinical image. For a clinical image that is a facial image and for a clinical image that is an intra-oral image, different orientation adjusting operations may be performed, so as to generate an adjusted image that is properly oriented.

There is provided a method and a system configured to compensate for image distortions.

Systems and methods for training an adapter network that adapts a model pre-trained on synthetic images to real-world data are disclosed herein. A system may include a processor and a memory in communication with the processor and having machine-readable that cause the processor to output, using a neural network, a predicted scene that includes a three-dimensional bounding box having pose information of an object, generate a rendered map of the object that includes a rendered shape of the object and a rendered surface normal of the object, and train the adapter network, which adapts the predicted scene to adjust for a deformation of the input image by comparing the rendered map to the output map acting as a ground truth.

A liveness detection method and apparatus, and a facial verification method and apparatus are disclosed. The liveness detection method includes detecting a face region in an input image, measuring characteristic information of the face region, adjusting the measured characteristic information in response to the characteristic information not satisfying a condition, and performing a liveness detection on the face region with the adjusted characteristic information upon the measured characteristic information not satisfying the condition.

A system includes an imaging device configured to capture image data of a monitored area and a computing device having at least one processor. The computing device processor is configured to receive first image data from the imaging device and second image data from a satellite imagery system, where the second image data relates to the monitored area. The processor is also configured to determine a first set of points of interest in the first image data and a second set of points of interest in the second image data, wherein members of each set of points of interest are represented by a respective vector. The processor is further configured to generate a homography matrix based on the two vectors and determine, using the homography matrix, latitude and longitude coordinates of at least one object represented in the first image data but not the second image data.