An imaging part obtains an image of a read object, and a character string recognizing part recognizes a character string in the image. An output format setting part sets an output format of a character string to be read from the image and to be output. A character extracting part obtains a candidate for a character string for output, at a portion matching the output format among the recognized character string, and if plural candidates are obtained, selects one of the plural candidates as the character string for output, based on a predetermined condition. A notifying part notifies a possibility of misreading in a case where plural candidates are obtained by the character extracting part.

An imaging part obtains an image of a read object, a shape recognizing part recognizes shapes in the image, and a character string recognizing part recognizes a character string among the recognized shapes. A notifying part notifies a possibility of misreading in a case where a shape, among the recognized shapes, not constituting any character exists in or near the recognized character string. When a character extracting part obtains a character string for output, that is all or a part of the recognized character string, the notification of the possibility of misreading may be performed also in a case where a shape not constituting any character exists in or near the obtained character string for output.

In a character string reading device, an imaging part obtains an image of a read object, and an output format setting part sets an output format of a character string to be read from the image and to be output. A character recognition condition setting part sets a character recognition condition, that only a group of characters including all characters defined by the output format are to be identified, among characters which the character string reading device can identify. A character string recognizing part recognizes a character string in the image according to the character recognition condition. A character extracting part obtains a character string for output, at a portion matching the output format among the recognized character string.

Provided is an information processing apparatus including: a character recognition unit configured to perform a character recognition process on an image of a processing target document; a generation unit configured to generate an instruction message based on a result of the character recognition process, the instruction message being a message for causing a large language model to reply a first character string corresponding to a predetermined item which is included in the document; a transmission unit configured to transmit the instruction message in order to obtain a reply to the instruction message from the large language model; and a reception unit configured to receive the reply to the instruction message from the large language model.

Systems and methods for deep learning model development for data extraction using a feedback loop. A system generates an interactive graphical user interface (GUI) on one or more user devices for displaying a document with data extracted from the document by a data extraction model together with a user interaction tool allowing the user to correct the extracted data. The system receives, via the interactive GUI, correction information for the extracted data and monitoring performance characteristics of the extraction model in real-time based on the user correction information. The system automatically updates and trains the extraction model using the correction information responsive to detecting that the performance characteristics meet a predetermined performance reduction condition.

Methods and systems for predicting medical signatures are provided. The methods and systems access a communication comprising a medicinal drug prescription associated with a patient, the medicinal drug prescription being provided in the communication by a healthcare professional. The methods and systems process, by a first machine learning model, the communication to extract a plurality of entities from the medicinal drug prescription and apply a second machine learning model to the plurality of entities to predict a medical signature. The predicted medical signature comprises directions for the patient to follow to take the medicinal drug. The methods and systems cause a label with the medical signature to be printed and applied to a container that includes the medicinal drug.

A system for scanning and validating clinical order device configurations is disclosed. A test instance of an infusion device is created based on a request, and an automated programming command is transmitted to the test instance. The automated programming command includes validation information for validating clinical order data, and a programming response is generated by the test instance based on the automated programming command, and provided for storage in a records system. In some implementations, the response includes an image, or reference to the image, of a graphical user interface that would be presented by the infusion device configured according to the validation information.

A system to create a machine-readable code includes a digital camera; a computer including a processor communicatively coupled to the digital camera; and a monitor to display a graphical user interface provided by the computer, wherein the processor is configured to execute an application stored in a memory that is communicatively coupled to the processor, the application, when executed, causing the processor to: command the digital camera to capture a first image of identification information marked on an article, perform optical character recognition of the first image to identify characters of the identification information and create therefrom a second image, display the first image and the second image via the graphical user interface on the monitor so that the first image can be compared to the second image, and create a machine-readable code representing the identification information.

Systems and methods for augmented-reality tutoring can utilize optical character recognition, natural language processing, and/or augmented-reality rendering for providing real-time notifications for completing a determined task. The systems and methods can include utilizing one or more machine-learned models trained for quantitative reasoning and can include providing a plurality of different user interface elements at different times.

The devices, systems, and methods described herein are directed to obtaining user feedback regarding an image containing text and graphics. In some examples, the image is analyzed to detect one or more portions of the image that contain text, which are sequentially displayed to a user. The user provides feedback regarding the text. Each of the portions of the image that contain graphics are displayed to the user, and the user provides feedback regarding the graphics. In some examples, the text and/or graphics are rotated and/or enlarged before being displayed to the user.