A control unit of an image inspection apparatus executes the OCR tool as the inspection tool. When the OCR tool is executed, the control unit inputs a workpiece image including a character image region to the neural network, and acquires a character type recognition result indicating a character type of a unit character image region and a position of the unit character image region based on information related to a character type recognizable by the OCR tool. Further, the control unit calculates a quality score indicating a degree of similarity between a master image corresponding to a character type indicated in the character type recognition result and an image of the unit character image region based on the position of the unit character image region, and outputs the quality score in association with the character type indicated in the character type recognition result.

In some implementations, a device may receive an image of a document, the image depicting a reference feature associated with the document, the reference feature including at least one of: a face of a person, a machine-readable code, or a text field. The device may identify a rotational angle of the reference feature as depicted in the image based on comparing the reference feature as depicted in the image to one or more orientation parameters of the reference feature associated with a display orientation associated with the document. The device may rotate the image of the document by an angle to obtain an orientated image of the document, the angle being based on the rotational angle of the reference feature as depicted in the image. The device may provide the orientated image of the document for display.

License plate recognition (LPR) systems may encounter degradation in quality and obstructions that negatively impact performance of the LPR systems. A LPR system is configured to apply image processing algorithms to output information describing performance of the system and to monitor the performance of the system over time. Based on the performance of the system over time, the LPR system determines when one or more entities of the system require action to maintain or improve performance and transmits information describing the required action.

The disclosure includes a system and method for determining a first measure of blur value associated with a first portion of a document under test; determining a second measure of blur value associated with a second portion of the document under test; determining whether an inconsistency in a set measure of blur values associated with the document under test is present, wherein the set of measure of blur values associated with the document under test includes the first measure of blur value and the second measure of blur value; and modifying a likelihood that the document is accepted or rejected based on whether the inconsistency is absent or present, respectively.

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 LIDAR managed live video stream of image data of a field of view of at least one camera, wherein the live video stream includes high quality confidence scored 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 of each side of the financial instrument from the live video stream of image data. The extracted data fields are communicated to a remote deposit server to complete the remote deposit.

This specification describes methods and systems for printed code inspection. For instance, the specification describes a computer-implemented method for printed code inspection by a printed code inspection system operating in conjunction with a production line apparatus configured to move objects along a production line comprising: receiving an image of an object to which a printed code comprising one or more printed characters should have been applied, the image having been captured when the object was located at a particular position on the production line; analysing the image to detect, based on a set of one or more character identification parameters, at least one candidate character within the image; determining, for each of the at least one candidate characters and based on a set of one or more candidate character properties, a likelihood that the candidate character is one of the printed characters of the printed code that should have been applied to the object; determining, based on the candidate characters determined as being likely to be one of the printed characters of the printed code that should have been applied to the object, whether the printed code is present and legible on the object; and outputting an indication as to whether the printed code that should have been applied to the object is present and legible on the object.

Systems and methods for processing document images using large language models to extract a key/value pair. The method includes a four-stage framework: (1) Image Quality Evaluation, assessing image attributes like text legibility and sharpness; (2) Image Classification, categorizing documents into predefined types; (3) Key/Value Pair Extraction, identifying relevant data fields; and (4) Extraction Evaluation, assigning confidence scores based on one or more predetermined criteria. The process employs prompt engineering to configure structured prompts for guiding the model at each stage. Outputs, including confidence scores and extracted data, are formatted for integration with downstream workflows, enabling applications in claims processing, invoicing, and other document-centric tasks.

Methods and systems are described herein for identifying the location and nature of any blur within one or more images received as a user communication and generating an appropriate correction. The system utilizes a first machine learning model, which is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information. The system may apply a corrective action selected by the first machine learning model, which may comprise stitching blurred images together to a sharp product image and/or some other method appropriate for rectifying images received.

Systems, apparatus, articles of manufacture, and methods are disclosed to label data. An example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to separate label data in a first data set from portions of an image, generate candidate labeled data based on associated ones of unlabeled portions of the image and optical character recognition (OCR) data, generate key performance indicator (KPI) metric values based on a comparison between the candidate labeled data and a second data set, and adjust weights of a model based on the KPI metric values.

A training image in accordance with a way a hane occurs, which is found in actual handwriting, is generated. Among line segments constituting a handwritten character in a character image representing the handwritten character, a line segment at which a handwritten hane may occur is detected. Then, by performing processing to add a simulated hane to the end portion of the detected line segment, a training image is generated.