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 for using a mobile device to submit an application for an insurance policy using images of documents captured by the mobile device are provided herein. The information is then used by an insurance company to generate a quote which is then displayed to the user on the mobile device. A user captures images of one or more documents containing information needed to complete an insurance application, after which the information on the documents is extracted and sent to the insurance company where a quote for the insurance policy can be developed. The quote can then be transmitted back to the user. Applications on the mobile device are configured to capture images of the documents needed for an insurance application, such as a driver's license, insurance information card or a vehicle identification number (VIN). The images are then processed to extract the information needed for the insurance application.

Systems and methods for using a mobile device to submit an application for an insurance policy using images of documents captured by the mobile device are provided herein. The information is then used by an insurance company to generate a quote which is then displayed to the user on the mobile device. A user captures images of one or more documents containing information needed to complete an insurance application, after which the information on the documents is extracted and sent to the insurance company where a quote for the insurance policy can be developed. The quote can then be transmitted back to the user. Applications on the mobile device are configured to capture images of the documents needed for an insurance application, such as a driver's license, insurance information card or a vehicle identification number (VIN). The images are then processed to extract the information needed for the insurance application.

The present disclosure relates to technology for automatically searching and recovering the recovery area of frames corresponding to a desired time for large-capacity video evidence using a time map generated through an optical character recognition (OCR) function. A digital forensic apparatus for searching and recovering a recovery target area for large-capacity video evidence using a time map according to an embodiment of the present disclosure may include a division recovery device for collecting video evidence from a storage device, dividing the collected video evidence into a plurality of spaces in consideration of the physical space of the storage device, and recovering a representative frame in each of the divided spaces; a time information recognizer for recognizing time information from the recovered representative frame using an optical character recognition (OCR) function; a time map generator for generating a time map in which the divided spaces are arranged according to a time criterion based on the recognized time information; and a selective recovery device for searching a recovery target area by matching specific time information input by a user with the generated time map and recovering the searched recovery target area.

The present disclosure relates to technology for automatically searching and recovering the recovery area of frames corresponding to a desired time for large-capacity video evidence using a time map generated through an optical character recognition (OCR) function. A digital forensic apparatus for searching and recovering a recovery target area for large-capacity video evidence using a time map according to an embodiment of the present disclosure may include a division recovery device for collecting video evidence from a storage device, dividing the collected video evidence into a plurality of spaces in consideration of the physical space of the storage device, and recovering a representative frame in each of the divided spaces; a time information recognizer for recognizing time information from the recovered representative frame using an optical character recognition (OCR) function; a time map generator for generating a time map in which the divided spaces are arranged according to a time criterion based on the recognized time information; and a selective recovery device for searching a recovery target area by matching specific time information input by a user with the generated time map and recovering the searched recovery target area.

The present disclosure relates to a medical image analysis method using a processor and a memory which are hardware. The method includes generating predicted second metadata for a medical image by using a prediction model, and determining a processing method of the medical image based on one of first metadata stored corresponding to the medical image and the second metadata.

The present disclosure relates to a medical image analysis method using a processor and a memory which are hardware. The method includes generating predicted second metadata for a medical image by using a prediction model, and determining a processing method of the medical image based on one of first metadata stored corresponding to the medical image and the second metadata.

Disclosed herein are system, method, and computer program product embodiments for optical character recognition (OCR) pre-processing using machine learning. In an embodiment, a neural network may be trained to identify a standardized document rotation and scale expected by an OCR service performing character recognition. The neural network may then analyze a received document image to identify a corresponding rotation and scale of the document image relative to the expected standardized values. In response to this identification, the document image may be modified in the inverse to standardize the rotation and scale of the document image to match the format expected by the OCR service. In some embodiments, a neural network may perform the standardization as well as the character recognition using a shared computation graph.

A text recognition method includes scaling a to-be-recognized image based on a first scale ratio, determining first coordinate information corresponding to a text line area in the scaled to-be-recognized image, determining, based on the first scale ratio, second coordinate information corresponding to the first coordinate information, where the second coordinate information is coordinate information of the text line area in the to-be-recognized image, performing character recognition on text line images corresponding to the second coordinate information by using a recognition model, and determining text line content corresponding to the text line images, where the to-be-recognized image includes the text line images.

Systems and methods for using a mobile device to submit an application for an insurance policy using images of documents captured by the mobile device are provided herein. The information is then used by an insurance company to generate a quote which is then displayed to the user on the mobile device. A user captures images of one or more documents containing information needed to complete an insurance application, after which the information on the documents is extracted and sent to the insurance company where a quote for the insurance policy can be developed. The quote can then be transmitted back to the user. Applications on the mobile device are configured to capture images of the documents needed for an insurance application, such as a driver's license, insurance information card or a vehicle identification number (VIN). The images are then processed to extract the information needed for the insurance application.