Systems, methods, and devices for automated self-learning machine data normalization, digitization, and extraction for verification and notification are disclosed herein. In some embodiments, a computer-implemented self-learning method for dynamically transmitting electronically a registration authorization request includes accessing an electronic vehicular database to electronically retrieve source data objects, applying an algorithm to the electronically retrieved source data objects, extracting data fields from the source data objects, normalizing the extracted data fields from the source data objects, dynamically selecting a first remotely connected electronic vehicular authorization database, accessing first remotely connected electronic vehicular authorization database-specific vehicular interchange requirements, and electronically transmitting at least one of the electronically retrieved source data objects to the dynamically selected first remotely connected electronic vehicular authorization database based on the source data objects, satisfying the first remotely connected electronic vehicular authorization database-specific vehicular interchange requirements.

In a case where a single image is formed by reading one or more reading sheets multiple times, and if the reading sheets are not appropriately placed on a document platen, images of the plurality of reading sheets formed in the single image do not match each other in up, down, left, and right or vertical and horizontal relationships desired by a user. An image forming apparatus includes a display unit configured to, based on a direction of a character string obtained from an image obtained by reading a first surface and a direction of a character string obtained from an image obtained by reading a second surface, display information regarding whether a direction of a reading sheet relative to a predetermined reference is excellent or poor.

This application provides a method of detecting printing defects. The method includes obtaining a first image of each character in a reference image. A third image of each character is obtained based on the first image of each character, a fourth image of each character is obtained based on a second image of each character obtained from an image to be detected. Once a fifth image of each character is obtained based on the third image of each character, a sixth image of each character is obtained according to the fourth image and the fifth image of each character, a detection result of each character in the image to be detected is determined according to the fifth image and the sixth image of the each character.

There are provided systems and methods for image repair of captured document images for document image submissions using generative artificial intelligence (AI). A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users, which may be used to engage in interactions with other users and entities including for electronic transaction processing. When utilizing these services, document verification may be required to verify a document. A user may capture an image of a document, such as a driver's license, and an image repair generative AI may determine if image repair may be necessary to improve an image quality of the image. The generative AI may be trained using multiple neural networks to generate and distinguish between different image qualities. The generative AI may therefore generate image data that improves the image quality and allows for verification of content in the image.

The present disclosure prevents learning a miscorrection made by a user on an OCR result. Provided is an information processing apparatus including: a character recognition unit configured to extract a character string containing at least one character by performing character recognition on an image obtained by scanning of a document; a display control unit configured to display the extracted character string on a display apparatus; an obtaining unit configured to receive a user correction on the extracted character string and obtaining a corrected character string based on the user correction; and a learning unit configured to learn a correction characteristic of a difference portion between the extracted character string and the corrected character string. The learning unit does not learn the correction characteristic of the difference portion in a case where a reliability level of the character recognition on the difference portion is equal to or above a predetermined threshold.

A method for reading an optical prescription on an optical prescription image. The method includes detecting a region comprising the optical prescription on the optical prescription image; extracting the optical prescription and converting the optical prescription into machine-encoded optical prescription data; classifying a portion of the optical prescription data into one or more predetermined categories, to generate an optical prescription value associated with a respective one of the one or more predetermined categories; and determining whether the optical prescription value associated with the respective one of the one or more predetermined categories contains an error, and, if the optical prescription value contains the error, correcting the error within the optical prescription value, to generate a corrected optical prescription value associated with the respective one of the one or more predetermined categories. A system for reading an optical prescription on an optical prescription is also disclosed.

A computer implemented method, system, and non-transitory computer-readable device that may be used in a remote deposit environment. Upon receiving a user request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a live video stream of image data of a field of view of at least one camera, wherein the live video stream includes imagery of at least a portion of each side of the financial instrument. The method continues by extracting data fields based on the formation of image objects on one or more sides of the financial instrument from the live video stream of image data. An EFT conversion of extracted data fields may be processed during or subsequent to the extraction process. A message is sent from a payee to a payor requesting the EFT. Upon acceptance, an EFT to the payee occurs. Upon denial, the remote deposit process is completed.

OCR-text correction system and method embodiments are described. The OCR-text correction embodiments comprise or cooperate with a transformer-based sequence-to-sequence language model. The model is pretrained to denoise corrupted text and is fine-tuned using OCR-correction-specific examples. Text obtained at least in part through OCR is applied to the fine-tuned pretrained transformer model to detect at least one error in a subset of the text. Responsive to detecting the at least one error, the fine-tuned pretrained transformer model outputs an updated subset of the text to correct the at least one error.

A printer includes a printing device and a controller. The controller of the printer acquires image data and detects character data indicating a character from the acquired image data. Further, the controller adds a broken line along an outline of the character indicated by the detected character data. The controller generates a multivalued image with multiple values based on an image including the character with the broken line added, converts the generated multivalued image into two gray scales, and generates a binarized image. Further, the controller causes the printing device to perform printing based on the generated binarized image.

An image processing apparatus comprises: an acquiring unit configured to acquire image data generated by reading an original; a specifying unit configured to specify an object region including a predetermined object in the image data; a removing unit configured to remove, from the object region, noise with a size smaller than a size specified by a first threshold; and a setting unit configured to set the first threshold, for each object region specified by the specifying unit, in accordance with the size of the object region.