Disclosed are techniques for building a dynamic dictionary and using the dictionary to remove phrases or words appearing in and out of context in a document. The techniques include, for example, receiving electronic health record (EHR) data, determining, using natural language processing (NLP), an instance of a personal health information (PHI) phrase in the EHR data based on a NLP system confidence metric being above a threshold, determining another instance of the PHI phrase in the EHR data that does not have the same context as the first context, removing the instances of the PHI phrase from the EHR data to produce cleaned EHR data, and taking an action based on the cleaned EHR data. The confidence metric can indicate likelihood that the PHI phrase is a PHI phrase and the metric can be based at least in part on a first context of the PHI phrase.

The present disclosure relates to a system and computer-implemented method for character recognition in a payment card. The method includes receiving an image of a payment card and one or more details associated with the payment card. Further, a derivative of the image is determined based on the one or more details and a horizontal sum of pixel values is determined for a plurality of rows in the image. Furthermore, one or more Regions of Interest (ROIs) are identified in the image by comparing the horizontal sum of pixel values with a predefined first threshold. Subsequently, one or more characters in the one or more ROIs are extracted using one or more peak values in a histogram of the one or more ROIs. Finally, each of the one or more characters extracted from the one or more ROIs is recognized using a trained Artificial Intelligence technique.

As an example, a server may receive, from a computing device, a submission created by an author. The submission includes book data associated with a book and author data associated with the author. The author data includes incarceration data indicating whether the author was incarcerated. The server may determine, based on the author data and the book data, that the submission is publishable. The server may create, based on the book data, a printable book, an e-book, and an audio book and make one or more of the printable book, the e-book, and the audio book available for acquisition.

Methods and systems for artificial intelligence (AI)-assisted document annotation and training of machine learning-based models for document data extraction are described. The methods and systems described herein take advantage of a continuous machine learning approach to create document processing pipelines that provide accurate and efficient data extraction from documents that include structured text, semi-structured text, unstructured text, or any combination thereof.

Methods, systems, and devices for automatically converting a static electronic file format and its various elements into a dynamic digital form with executable elements that can be customized before being used. The resulting digital form is compatible with digital workflows and processes. The disclosed systems, methods, and devices go beyond simply extracting data from the original electronic file format and instead enable users to, without using code, convert the source form into a dynamic, malleable digital form while still retaining the source form's original purpose and functionality.

A character recognition method includes inputting an input image of a document, with the input image including a plurality of characters; selecting the plurality of characters through an object detection module to form at least one character region; separating the plurality of characters in the at least one character region to form a plurality of character boxes; performing calculation to determine a format of a character in each of the plurality of character boxes; recognizing the characters in the at least one character region through an object recognition module to determine a symbol content of the character in each of the plurality of character boxes; and converting the plurality of characters according to the format and symbol content of the character in each of the plurality of character boxes, and outputting corresponding editable characters.

A license plate detection and recognition system receives training data comprising images of license plates. The system prepares ground truth data from the training data based predefined parameters. The system trains a first machine learning algorithm based on the ground truth data to generate a license plate detection model. The license plate detection model is configured to detect one or more regions in the images. The one or more regions contains a candidate for a license plate. The LPDR system generates a bounding box for each region. The LPDR system trains a second machine learning algorithm based on the ground truth data and the license plate detection model to generate a license plate recognition model. The license plate recognition model generates a sequence of alphanumeric characters with a level of recognition confidence for the sequence.

An image processing method for identifying text on production line components obtains an image to be recognized and a standard image for reference and extracts a first text area of the image to be recognized. A second text area of the standard image is obtained, and a text window is extracted based on the second text area. The method further obtains a target text area of the image to be recognized based on the first text area and the text window, and obtains a first set of first text sub-areas, and obtains a second set of second text sub-areas, by dividing the second text area into sub-windows of the text window. The method further marks the image to be recognized as a qualifying image when each first text sub-area of the first set is the same as a corresponding second text sub-area of the second set.

An image processing method for identifying text on production line components obtains an image to be recognized and a standard image for reference and extracts a first text area of the image to be recognized. A second text area of the standard image is obtained, and a text window is extracted based on the second text area. The method further obtains a target text area of the image to be recognized based on the first text area and the text window, and obtains a first set of first text sub-areas, and obtains a second set of second text sub-areas, by dividing the second text area into sub-windows of the text window. The method further marks the image to be recognized as a qualifying image when each first text sub-area of the first set is the same as a corresponding second text sub-area of the second set.

Systems and methods for secure and efficient bill capture, analysis, and execution are provided. A method may include capturing, via a camera embedded in a smart card, an image of a bill. The bill may include a plurality of text fields. The method may include processing the text fields via a microprocessor embedded in the smart card. The method may include determining, based at least in part on the processing of the text fields, a balance amount and a payment recipient associated with the bill. The method may also include executing a payment for the balance amount from an account associated with a user of the smart card to an account associated with the payment recipient. The executing may be performed via a wireless communication element embedded in the smart card which may be configured to provide wireless communication between the smart card and a payment gateway.