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.

Provided is a model generation system for generating a text line recognition model that recognizes a text line included in a text line image, the model generation system including a processor section, in which the text line recognition model includes a visual feature extractor and a language context relation network, the processor section determines a variable of the language context relation network by acquiring text data for training and thus training the language context relation network by using the acquired text data, determines a variable of the visual feature extractor by training the text line recognition model through the use of a labeled text line image while the variable of the language context relation network is fixed, and generates the text line recognition model while the variable of the language context relation network is set to the determined variable thereof and the variable of the visual feature extractor is set to the determined variable thereof.

Methods and systems are provided for labeling text data using one or more machine learning (ML) models. In one embodiment, a method for training an ML model to label text data comprises manually labeling one or more words in a portion of a first set of text data as instances of a predefined entity of interest; extracting one or more example phrases from the labeled portion of text data, submitting an instruction to a Large Language Model (LLM) to label instances of the predefined entity of interest in the first set of text data, the instruction including the one or more example phrases; and training an ML model to label instances of the predefined entity of interest in a second set of text data, using training data including labeled text data of the first set of text data, the labeled text data outputted by the LLM.

A method is disclosed for using an artificial neural network (ANN) for automated text generation, the method includes, a) receiving, through an interface of a computing device, one or more inputs, b) extracting data from the one or more inputs, resulting in extracted data, c) performing a mapping mechanism based on the extracted data, the mapping mechanism resulting in mapped data instances, d) training a first ANN based on at least a first set of mapped data instances, wherein the first set of mapped data instances require a similarity measurement, e) determining a weight for at least one encoder and at least one decoder, based on the training of the first ANN, f) providing, at the encoder, a sequence of mapped data instances, g) generating, at the decoder, based on at least a first set of the sequence of mapped data instances, a first processed text section, S, that corresponds to the first set of mapped data instances, h) determining if the first processed text section accurately corresponds to the first set of mapped data instances and i) generating, at the decoder, a revised processed text section rS, if the first processed text section in (g) does not accurately correspond to the mapped data instances.

An image processing apparatus includes an input unit configured to input image data, a learning unit configured to perform machine learning processing using information contained in the image data input by the input unit, an estimation unit configured to output an estimation result based on the information contained in the image data using a learning model generated by learning of the learning unit, and a determination unit configured to determine whether the image data input by the input unit contains sensitive information, wherein in a case where the determination unit determines that the image data input by the input unit contains the sensitive information, the learning unit does not perform machine learning on at least the sensitive information contained in the image data.

A data uploader uploads a first plurality of data values associated with a transaction. A document uploader uploads digital images of a plurality of documents associated with the transaction, wherein a second plurality of data values are embedded in the documents. The digital images are stripped of numerical (or other) data values before being transmitted to a first machine learning engine to identify documents associated with the digital images. A second machine learning engine identifies categories of numerical data values (and other data values) that are included in the second plurality of data values and that are embedded in the documents. The first and second plurality of data values can be compared for accuracy and corrected. The first and second machine learning engine are sent bifurcated feedback regarding the respective identification performed by each to improve future accuracy.

Metadata may be identified for text generated by a generative machine learning model. A text is obtained and a weighting scheme determine for performing similarity analysis. Different similarity analysis techniques are performed that compare the text with representations of texts in the training data set for the generative machine learning model. Final similarity scores are generated that combine the different similarity analysis techniques according to the weighting scheme and are used to select metadata to provide that is relevant to the text.

Various embodiments offer improved functionality for generating and/or refining templates that can be used for automatically extracting information from within an invoice or other document, based on geometric characteristics of the document. An initial template may be automatically generated, and such initial template may then be refined over time based on user feedback, so as to improve reliability and accuracy in information extraction.