One embodiment provides a computer-implemented method that includes comparing, by a computing device, an input character signal with one or more prestored character templates for determining an estimated difference measure. The computing device, based on the estimated difference measure, determines one or more mixing weights between a stored character patch buffer and a current input character patch for determining an output mixing patch. The computing device further updates the character patch buffer based on the output mixing patch. The computing device additionally substitutes a designated area using the output mixing patch to produce a final output.

Systems and methods are provided for assessing whether mobile deposit processing engines meet specified standards for mobile deposit of financial documents. A mobile deposit processing engine (MDE) is evaluated to determine if it can perform technical capabilities for improving the quality of and extracting content from an image of a financial document. A verification process then begins, where the MDE performs the image quality enhancements and text extraction steps on sets of images from a test deck. The results of the processing of the test deck are then evaluated by comparing confidence levels with thresholds to determine if each set of images should be accepted or rejected. Further analysis determines whether any of the sets of images were falsely accepted or rejected in error. An overall error rate is then compared with minimum accuracy criteria, and if the criteria are met, the MDE meets the standard for mobile deposit.

A device comprising a printer configured to apply a code of printed content on a substrate of a product based on a printer technology type, the code having a plurality of digits. The device includes an optical code detector, executed by one or more processors, to detect the code in a received image of the product printed by the printer by optically recognizing characters in the received image using a trained optical character recognition (OCR) algorithm for the printer technology type. The OCR algorithm is trained to identify each digit of the plurality of digits of the code in a region of interest (ROI) based on at least one product parameter to which the printed content is directly applied and the printer technology type. A system and method are also provided.

An image processing system according to an embodiment includes a cart having a first processor and a camera mounted on the cart. The camera photographs an object and generates an image. The first processor corrects focus of the camera based on correction information to bring the camera into focus with the object to be photographed. A second processor: calculates a standard deviation and an entropy based on tone information of pixels in the image, calculates a ratio between the standard deviation and the entropy, compares the ratio and a reference value, determines the correction information based on the comparison result, and provides the correction information to the first processor.

Systems and methods for automatic enrollment and identity verification based upon processing a captured image of a document are disclosed herein. Various embodiments enable, for example, a user to enroll in a particular service by taking a photograph of a particular document (e.g., his driver license) with a mobile device. One or more algorithms can then extract relevant data from the captured image. The extracted data (e.g., the person's name, gender, date of birth, height, weight, etc.) can then be used to automatically populate various fields of an enrollment application, thereby reducing the amount of information that the user has to manually input into his mobile device in order to complete the enrollment process. In some embodiments, a set of internal and/or external checks can be run against the data to ensure that the data is valid, has been read correctly, and is consistent with other data.

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.

Provided herein are systems and methods for extracting text from a document. Different optical character recognition (OCR) tools are used to extract different versions of the text in the document. Metrics evaluating the quality of the extracted text are compared to identify and select higher quality extracted text. A selected portion of text is compared to a threshold to ensure minimal quality. The selected portion of text is then saved. Error correction can be applied to the selected portion of text based on errors specific to the OCR tools or the document contents.

Disclosed embodiments may include a system for measuring document legibility. The system may automatically receive document image data from a user device. The system may then process the image data using optical character recognition to create language data containing a plurality of words. The system may then obtain an overall number by counting the plurality of words in the language data. The system may then identify and count the common words within the plurality of words by comparing the plurality of words to words in a database. A score may be obtained by dividing the common word number by the overall number. The score may then be compared to a legibility threshold. If the score is below the threshold, the system may determine the document is illegible. If the score is above the threshold, the system may determine the document is legible.

A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

Systems and methods are provided for assessing whether mobile deposit processing engines meet specified standards for mobile deposit of financial documents. A mobile deposit processing engine (MDE) is evaluated to determine if it can perform technical capabilities for improving the quality of and extracting content from an image of a financial document. A verification process then begins, where the MDE performs the image quality enhancements and text extraction steps on sets of images from a test deck. The results of the processing of the test deck are then evaluated by comparing confidence levels with thresholds to determine if each set of images should be accepted or rejected. Further analysis determines whether any of the sets of images were falsely accepted or rejected in error. An overall error rate is then compared with minimum accuracy criteria, and if the criteria are met, the MDE meets the standard for mobile deposit.