2084results about "Character recognition" patented technology

A computer implemented method, system and non-transitory computer-readable device for a remote deposit environment activating, on a client device, a financial application, wherein the financial application is configured to instantiate a customer interface (UI) on the client device. Upon receiving a customer request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a live stream of image data of a field of view of at least one camera, wherein the live stream includes imagery of at least a portion of the financial instrument. The method continues by extracting, based on the live stream of image data and by an optical character recognition program resident on the client device, one or more data fields from the financial instrument and communicating the extracted one or more data fields to a remote deposit server to complete the electronic deposit.

A computer implemented method, system and non-transitory computer-readable device for a remote deposit environment. Upon receiving a user request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a live stream of image data of a field of view of at least one camera, wherein the live stream includes imagery of at least a portion of the financial instrument. The method continues by extracting in real-time, based on the formation of byte array objects from the live stream of image data, data fields from a ranked sequence of imagery to be processed by an optical character recognition (OCR) program resident on the client device. The OCR process extracts one or more data fields from of the financial instrument that are communicated to a remote deposit server to complete the electronic deposit.

A system and method for managing greenhouse gas emissions is described. In one aspect, the system and method are configured to receive real-time emission data from distributed data sources, including IoT sensors, optical character recognition systems, and augmented reality measurement devices. ML models are trained using ML engines based on historical data and the received emission data. The trained models process and validate the real-time emission data using ML engines. Timestamp blocks are generated using a proof of history blockchain architecture with a variable delay function. The validated emission data is stored and verified in the generated timestamp blocks. The verified emission data is transformed into standardized carbon credit tokens. Visualizations of greenhouse gas emissions are displayed on a user interface, showing information from a real-time emission report. The system and method enable automated collection, validation, storage, and visualization of greenhouse gas emission data using blockchain and ML technologies.

A computer-implemented method is provided for a mobile device to detect, by a camera of the mobile device, a plurality of checks; determine, by a processing unit of the mobile device, that the image of the plurality of checks is of sufficient quality; instruct, by a display of the mobile device, a user to take a photograph of the plurality of checks; crop, by the processing unit, the photograph of the plurality of checks into a plurality of images, wherein each of the plurality of images contains one of the plurality of checks; and transmit, by a transmitter of the mobile device, the plurality of images to a server via a network. The plurality of images may be transmitted individually (i.e., one at time), or alternatively, collectively and in one payload.

Systems and methods for reducing the search space by processing media content to refine search parameters. A computing device may obtain the media content in response to receiving a request for inclusion of the media content in a media content knowledge repository, extract an audio component, a video component, and a text component of the media content, and determine attributes within the extracted components. The computing device may determine segment attributes based on a result of correlating the determined audio, video, and text attributes, integrate the segment attributes into the media content knowledge repository, and / or perform any of a variety of responsive actions.

A data extraction service is disclosed. The data extraction service may receive an image that includes a data table. The data extraction service may perform optical character recognition on the image. The data extraction service may label text in the image to identify the table. The data extraction service may generate a digital data table corresponding to the data table in the image. The data extraction service may format the digital data table and provide the formatted digital data table to a downstream entity.

Various embodiments include methods for generating image datasets for training an artificial intelligence machine learning (AI / ML) optical character recognition (OCR) model. Image processing may be performed on a plurality of roadway images to identify street signs within the images and generate a dataset of sign images categorized into sign variants of the same shape, color, pictogram, and characters. An OCR model may process sign images to obtain OCR results for images of each sign variant. An aggregation process may be performed on the OCR results for all sign images within each sign variant to identify a ground truth OCR result for each sign variant. The ground truth OCR result may be used to automatically label all sign images of each sign variant to produce an OCR model training dataset. The produced training dataset may then be used to retrain the initial AI / ML OCR model and / or train other AI / ML OCR models.

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.

The invention provides a power grid wiring diagram automatic analysis system and method based on deep learning. The method comprises the steps of S1, data preprocessing and model training; s2, carrying out equipment identification and association; step S3, topology reasoning and verification are carried out; s4, standardized output: converting the verified power grid network model into a regulation and control cloud platform standard graphic file, and optimizing graphic arrangement through an adaptive layout algorithm; step S5, performing closed-loop optimization; and S6, performing manual intervention and iteration. According to the method, the wiring diagram analysis efficiency and accuracy can be remarkably improved, manual intervention is reduced, standardized graph generation of a power grid regulation and control cloud platform is supported, and a reliable data basis is provided for power system analysis and decision making.

A computer implemented method, system and non-transitory computer-readable device for a remote deposit environment activating, on a client device, a financial application, wherein the financial application is configured to instantiate a customer interface (UI) on the client device. Upon receiving a customer request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a live stream of image data of a field of view of at least one camera, wherein the live stream includes imagery of at least a portion of the financial instrument. The method continues by blending common pixels from the imagery to form a blended image, and extracting by an optical character recognition program one or more data fields from the blended image of the financial instrument.

Disclosed is a new document processing solution that combines the powers of machine learning and deep learning and leverages the knowledge of a knowledge base. Textual information in an input image of a document can be converted to semantic information utilizing the knowledge base. A semantic image can then be generated utilizing the semantic information and geometries of the textual information. The semantic information can be coded by semantic type determined utilizing the knowledge base and positioned in the semantic image utilizing the geometries of the textual information. A region-based convolutional neural network (R-CNN) can be trained to extract regions from the semantic image utilizing the coded semantic information and the geometries. The regions can be mapped to the textual information for classification / data extraction. With semantic images, the number of samples and time needed to train the R-CNN for document processing can be significantly reduced.

Systems and methods for reducing the search space by processing media content to refine search parameters. A computing device may obtain the media content in response to receiving a request for inclusion of the media content in a media content knowledge repository, extract an audio component, a video component, and a text component of the media content, and determine attributes within the extracted components. The computing device may determine segment attributes based on a result of correlating the determined audio, video, and text attributes, integrate the segment attributes into the media content knowledge repository, and / or perform any of a variety of responsive actions.

The invention discloses a mathematical ancient book illustration identification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a mathematical ancient book illustration; dividing the mathematical ancient book illustration into a character area, a formula area and an image area, and recording respective position information of the three parts as first position information; carrying out OCR (Optical Character Recognition) on the character region, carrying out formula restoration on the formula region, and restoring the image region; and performing layout reconstruction on the math ancient book illustration according to the text identified by the OCR, the restored formula, the restored image and the first position information to obtain a math ancient book illustration identification result, thereby realizing effective identification and digitization of the math ancient book illustration.

An electronic method for contextual task creation during a screen sharing session includes collecting shared screen data, searching the shared screen data for a potential task, and confirming the potential task with a user to generate a confirmed task. The electronic method can further include sending the confirmed task to a task management engine where the task can be collaboratively reviewed for progress and accuracy. The electronic method can be performed by a contextual task creation program or system that utilizes optical character recognition techniques for converting an image of text into a machine-readable text format, and natural language processing techniques for identifying keywords, entities, and actionable items within the text to provide possible task suggestions to a user.

A defect detection system includes one or more route optical sensors configured to generate image data depicting an underbody of a vehicle that is on a route. The defect detection system also includes a controller configured to input the image data generated by the one or more route optical sensors into a first machine learning algorithm that determines an area of interest in the image data. The area of interest contains equipment of interest. The controller is configured to input the image data that is within the area of interest into a second machine learning algorithm that detects a defect on the equipment of interest. The controller is configured to perform at least one responsive action based on a type of defect that is detected.

Disclosed herein are system, apparatus, device, method and / or computer program product embodiments for identifying, in a remote deposit system, a level of correspondence of a financial instrument with past deposit activity. The level of correspondence may be used to determine a funds availability schedule and / or identify counterfeit financial instruments. In some embodiments, the level of correspondence may be determined by comparing a date associated with the financial instrument to a date parameter, an amount of the financial instrument to an amount parameter, and / or one or more financial instrument characteristics with one or more payer-specific financial instrument characteristics.

The disclosed concepts relate to automatically identifying conditions in a video game to trigger a help session. When a help session is triggered, another video game player or machine learning model can temporarily take over for the current video game player until an ending condition is reached. Help session triggering can be designated by evaluation of prior gameplay data of other video game players to identify in-game conditions that may tend to cause user disengagement, such as in-game conditions that are associated with difficult in-game goals or negative in-game consequences.

A machine learning based computing method for extracting data from electronic documents, is disclosed. The machine learning based computing method includes steps of: receiving the electronic documents from data sources; converting formats of the electronic documents into first images associated with the electronic documents; classifying the first images associated with the electronic documents as second images including tabular data, and third images including non-tabular data, based on a CNN based machine learning model; determining tabular regions in tabular data associated with the second images by identifying bounding box coordinates indicating tabular regions in tabular data, based on a YOLO architecture based machine learning model; extracting first data from the tabular regions; extracting second data from non-tabular regions; and providing an output of the first data and the second data to second users on a user interface associated with electronic devices.

The invention discloses an invoice automatic auditing and intelligent settlement system for automobile road rescue, and relates to the technical field of intelligent settlement systems, a correction module integrates a machine learning model to analyze historical bills, automatically corrects unreasonable settlement amount and generates a correction suggestion report, a prompt module builds an intelligent priority matrix, and the intelligent priority matrix is sent to a server. The reminding frequency is dynamically adjusted according to parameters such as the historical settlement scale and the service life of a supplier, the settlement module recognizes an abnormal invoicing terminal by combining an invoicing equipment fingerprint recognition technology, a payment process is automatically triggered after verification is passed, a supplier credit portrait system is established, and a dynamic settlement scheme is supported. The settlement system establishes an intelligent priority matrix to realize important supplier high-frequency reminding and low-risk project low-frequency intervention, so that the whole process timeliness is guaranteed, the defects of a traditional rule engine in coping with complex scenes are effectively overcome, and the response capability and the autonomous decision-making capability of the system to abnormal behaviors are improved.

This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, the methods addresses traffic sign recognition as a language model-based image reasoning task by utilizing a multi-modal transformer architecture that combines the strength of vision and language machine learning (ML) models. The transformer architecture recognizes traffic signs based on visual features and associated taxonomy of the traffic signs. In a second aspect, the methods leverage context surrounding an autonomous vehicle through a graph-based modeling framework that fuses outputs from multiple perception modules to construct a semantic scene graph representation of an intersection, which consolidates processing diverse data types for traffic light relevancy detection. Other aspects and features are also claimed and described.

In an approach, a processor performs document layout analysis on a document generating a plurality of textual regions; extracts characteristics from each of the plurality of textual regions and associates the respective characteristics to the respective textual region as metadata; classifies each of the plurality of textual regions as an optical character recognition (OCR) region, non-OCR valuable region, or non-OCR non-valuable region using a classifier; performs OCR on each OCR region generating an OCR output; identifies associated constant OCR data from a constant OCR data repository for each non-OCR valuable region; merges the associated constant OCR data with the OCR output generating a complete OCR data for the received document; performs data extraction on the complete OCR data to identify data fields and key-value pairs generating extracted data; and determines whether the extracted data is valid based on a set of rules.

A trained machine learning model and a training dataset used to train the trained machine learning model can be received. Based on the training dataset, unsupervised adversarial examples can be generated. Robustness of the trained machine learning model can be determined using the generated unsupervised adversarial examples. The training dataset can be augmented with the generated unsupervised adversarial examples. The trained machine learning model can be retrained using the augmented training dataset.

A page identification technique is configurable to select regions of a screen for optical character recognition. The size of the regions may be selected to reduce the CPU resources needed to identify a page. Pre-processing and post-processing may be performed to improve the accuracy of the optical character recognition. A word mapping algorithm may include logical condition to determine a page name.

A computer implemented method, system and non-transitory computer-readable device for a remote deposit environment activating, on a client device, a financial application, wherein the financial application is configured to instantiate a customer interface (UI) on the client device. Upon receiving a customer request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by generating a live stream of image data of a field of view of at least one camera, wherein the live stream of image data includes imagery of at least a portion of the financial instrument, determining, based on the live stream of image data and a machine learning model (ML), an impermissibility score of the financial instrument and, based on exceeding a selectable threshold of the impermissibility score, modifying a status of the remote deposit to pause or terminate the remote deposit.

A method of extracting meta-data from a document includes capturing style attributes from the document, identifying cell-wise location coordinates for text characters using page segmentation and border table extraction, and finding relationship between nearby cells using surrounding embedding by determining shortest distant text cell in top, left, right, and bottom direction. The method further includes applying Graph Convolution Network with Informative Attention (GCN-IA) for providing more attention to informative nodes for generating better representation of surrounding embedding and capturing a deep contextual meaning from text cells. A domain specific language model is utilized and improved by a domain aware tokenizer. The method includes capturing a complex visual layout of the document using the domain specific visual model, determining meta-data information, representing linguistic and visual contexts of the document, and correcting the extracted output by applying advanced-post processing on the extracted output from advanced language-visual model.

Automatic data transfer between a source and a target using semantic artificial intelligence (AI) for robotic process automation (RPA) is disclosed. A user may be provided with the option of selecting a source and a target and indicating through an intuitive user interface that he or she would like to copy data from the source to the destination, regardless of format. This may be done at design time or at run time. For instance, the source and / or target may be a web page, a graphical user interface (GUI) of an application, an image, a file explorer, a spreadsheet, a relational database, a flat file source, any other suitable format, or any combination thereof. The source and the target may have different formats. The source, target, or both may not necessarily be visible to the user.

Systems and methods for tracking inventory items in an area of real space are disclosed. The method includes receiving a signal generated in dependence on sensors. The signal indicates a change to a portion of an image of an area of real space. The method includes, in response to receiving the signal, implementing a trained location detection model to determine, based on inputs, whether an inventory item identified in the portion of the image has changed a position in the area of real space. The method includes implementing a trained item classification model to determine a classification of the inventory item. The method includes updating an inventory database with inventory item data determined in dependence on the classification of the inventory item to provide an updated map of the area of real space as a result of the received signal indicating the change to the portion of the image.

Systems and processes for evaluating algorithms for aligning weakly-annotated data to recognized characters in a document are provided. In a method for evaluating an algorithm for aligning annotation data to recognized characters, strong annotations and weak-to-strong annotations, which are generated by applying a weak-to-strong annotation alignment algorithm, for a document are received and matched to generate respective pairs of matched annotations. For each pair of matched annotations, respective metrics are calculated including comparisons of aspects of the strong annotations to the weak-to-strong annotations. The respective metrics are aggregated, and an indication of the aggregated metrics are output to a graphical user interface or targeted application. Aggregated metrics determined for different weak-to-strong annotation alignment algorithms may be compared in order to select or adjust an algorithm to be used for Optical Character Recognition (OCR) operations.

A system and method are provided for automating the reformatting of graphic narratives (e.g., comic books, manga, etc.) to a digital format. An artificial intelligence (AI) based method identifies panels within the pages, and, based on the relative positions of the panels and the contextual content represented in segmented image and textual elements, the AI-based method predicts a narrative flow among the panels (and within some of the respective panels). Editors either approve or modify the predicted narrative flow. Using the narrative flow, a dynamic path is created to guide the reader's attention through the graphic narrative (e.g., using visual cues and / or an order in which the panels are displayed on a digital device (e.g., an e-reader). The narrative flow can also be used to make the reader's experience more engaging and immersive.