Embodiments of the invention include systems, methods, and computer-program products for providing an internal digital encoding as an overlay of a data element on a resource distribution instrument for white space correction. The invention identifies a type of resource distribution instrument with white space or insufficiencies and queue it for encoding. In this way, the encoding may be a layer for a user to view the data element to confirm the resource associated with the resource distribution instrument. The encoding may be embedded into the resource distribution instrument and removable upon external transmission.

For training data pairs comprising training text (a radiological report) and training images (radiological images associated with the radiological report), a first encoder network determines word embeddings for the training text. A concept is generated from the operation of layers of the first encoder network, which is regularized by a first loss between the generated concept and a labeled concept for the training text. A second encoder network determines features for the training image. A heatmap is generated from the operation of layers of the second encoder network, which is regularized by a second loss between the generated heatmap and a labeled heatmap for the training image. A categorical cross entropy loss is calculated between a diagnostic quality category (classified by an error encoder) and a labeled diagnostic quality category for the training data pair. A total loss function comprising the first, second, and categorical cross entropy losses is minimized.

One or more processors receives one or more first database records indicating a plurality of candidate identifiers are received by one or more processors. The one or more processors obtains one or more electronic images of the physical asset. At least one electronic image includes a graphical representation of data printed on the physical asset. The one or more processors identifies a plurality of regions of the data based on the one or more electronic images. The one or more processors determines a context of the physical asset based on the plurality of regions, selects the identifier from the plurality of candidate identifiers based on the plurality of regions, and generates a second database record including an indication of the context of the physical asset, and an indication of the identifier.

An example electronic system is described in which an imaging device includes a lens and an image sensor. The imaging device is aligned with an optical target. The optical target includes a text character of a defined text size. An image capturer activates the imaging device to capture an electronic image of the optical target. The electronic image includes the text character of the optical target. An optical recognizer generates an optical recognition result for the character based on the captured electronic image. A sharpness detector compares the optical recognition result with a true value of the text character included in the optical target. Based on the comparison, a designated or defined text size is selected as a designated resolution. The designated resolution is then associable with the imaging device, the optical target, the electronic image, or a component thereof.

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.

A mobile terminal includes a memory and a processor coupled to the memory, wherein the processor executes a process including acquiring a frame acquired by image capturing, detecting document position data of a document from the frame, determining a document type of the document, calculating image quality of document image data of the document on the basis of the document position data and the document type, determining whether the image quality is at a character recognizable level, and acquiring a frame acquired by the image recapturing when it is determined that the image quality is not at the character recognizable level.

One or more processors receives one or more first database records indicating a plurality of candidate identifiers are received by one or more processors. The one or more processors obtains one or more electronic images of the physical asset. At least one electronic image includes a graphical representation of data printed on the physical asset. The one or more processors identifies a plurality of regions of the data based on the one or more electronic images. The one or more processors determines a context of the physical asset based on the plurality of regions, selects the identifier from the plurality of candidate identifiers based on the plurality of regions, and generates a second database record including an indication of the context of the physical asset, and an indication of the identifier.

An apparatus includes a memory and a processor. The memory stores a dictionary and a machine learning algorithm trained to classify text. The processor receives an image of a page, converts the image into a set of text, and identifies a plurality of tokens within the text. Each token includes one or more contiguous characters that are both preceded and followed by whitespace within the text. The processor identifies invalid tokens by removing tokens of the plurality of tokens that correspond to words of the dictionary. The processor calculates, based on a ratio of a total number of valid tokens to a total number of tokens, a score. In response to determining that the score is greater than a threshold, the processor applies the machine learning algorithm to classify the text into a category and stores the image and/or text in a database according to the category.

An embodiment of the present invention is directed to a combination of two deep-learning computer vision modelscustomized with post-processingwrapped in a mobile application that is backed by an Application Programming Interface (API) supporting concurrent mobile users to accomplish asset serialization tasks in a warehouse or other storage environment.

In some implementations, a device may receive print data associated with a printer. The device may receive an image that depicts content that is printed on media by the printer. The device may determine, using a printhead analysis model, a status of a printhead of the printer based on the print data and a characteristic of the content, wherein the printhead analysis model is trained based on reference data associated with historical printing operations associated with one or more printers, wherein the reference data includes reference images associated with printed content from the historical printing operations and corresponding resistance measurements for one or more respective printheads of the one or more printers. The device may perform, based on the status, an action associated with the printhead of the printer.