Methods for label detection are disclosed herein. The method includes receiving, by a processor, an image of a label, detecting, by the processor, one or more physical characteristics of the label, determining, by the processor, one or more colors of the label, determining, by the processor, a data identifier for the one or more colors of the label, determining, by the processor, a product identifier associated with the label based on the data identifier, and generating, by the processor, a signal indicating a product to a user based on the product identifier

A system receives an image including a live facial image of the user and an identity document including a photograph of the user. Moreover, the system calculates a facial match score by comparing facial features in the live facial image to facial features in the photograph. The system recognizes data objects and characters in the identity document using optical character recognition (OCR) and computer vision, and then identifies, based on the recognized data objects and characters, a type of the identity document. Further, the system calculates a document validity score by comparing the recognized characters and data objects to character strings and data objects known to be present in the identified type of the identity document. Additionally, the system determines and outputs the user's identity verification status based on comparing the facial match score to a facial match threshold and comparing the document validity score to a document validity threshold.

According to one embodiment, a reading support system includes a processing device. The processing device includes an extractor and a type determiner. The extractor extracts a plurality of regions from a candidate region. The candidate region is a candidate of a region in which a meter is imaged. The regions respectively include a plurality of characters of the meter. The type determiner determines a type of the meter based on positions of the regions.

An interface information processing method and apparatus, a storage medium, and a device are provided. The method includes: displaying, based on a trigger operation on a floating translation component in a first display interface, a trigger progress in the floating translation component, the first display interface including a character of a first language type, the trigger progress being associated with trigger duration, and the trigger duration being a duration of the trigger operation on the floating translation component; and switching the first display interface to a second display interface based on the trigger progress in the floating translation component satisfying a full-screen translation start progress, the second display interface including a character of a second language type, and the character of the second language type being obtained by translating the character of the first language type.

Systems, methods and computer program products for image recognition in which instructions are executable by a processor to dynamically generate simulated documents and corresponding images, which are then used to train a fully convolutional neural network. A plurality of document components are provided, and the processor selects subsets of the document components. The document components in each subset are used to dynamically generate a corresponding simulated document and a simulated document image. The convolutional neural network processes the simulated document image to produce a recognition output. Information corresponding to the document components from which the image was generated is used as an expected output. The recognition output and expected output are compared, and weights of the convolutional neural network are adjusted based on the differences between them.

A method for image processing is disclosed. The method includes: obtaining an image including a check with a magnetic ink character recognition (MICR) code; generating a mask including a plurality of shapes based on the image and an estimated rotation angle of the check; generating a stroke width map (SWM) by applying a stroke width transform (SWT) to a plurality of regions in the image corresponding to the plurality of shapes; generating a first word line associated with a first region based on a plurality of words in the SWM; rotating a portion of the SWM associated with the first word line; and detecting, after rotating, the MICR code by applying a plurality of OCR processes to the portion of the SWM.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating and providing actionable data from newspaper articles identified and segmented from digital newspaper images. For example, the disclosed systems segment articles of a newspaper image by using specially designed models to generate polygons defining article boundaries within the newspaper image. In some cases, the disclosed systems further determine article text from a polygon of an article for additional processing to determine an article topic, determine an article type, predict entity names within the article, and/or predict a locality associated with the article.

Method and system of automatically extracting information of a predefined type from a document is provided. The method comprises using an object detection algorithm to identify at least one segment of the document that is likely to comprise the information of the predefined type. The method further comprises building at least one bounding box corresponding to the at least one segment and if the bounding box is likely to comprise the information of the predefined type extracting the information comprised by the bounding box from the at least one bounding box.

Systems and methods for image modification to increase contrast between text and non-text pixels within the image. In one embodiment, an original document image is scaled to a predetermined size for processing by a convolutional neural network. The convolutional neural network identifies a probability that each pixel in the scaled is text and generates a heat map of these probabilities. The heat map is then scaled back to the size of the original document image, and the probabilities in the heat map are used to adjust the intensities of the text and non-text pixels. For positive text, intensities of text pixels are reduced and intensities of non-text pixels are increased in order to increase the contrast of the text against the background of the image. Optical character recognition may then be performed on the contrast-adjusted image.

A computing device receives a request to render a listing of item entries on a graphical user interface. The computing device receives an electronic image of the document, analyzes the electronic image, and determines a document type by performing an image recognition on a first portion the electronic image, comparing information extrapolated via the image recognition algorithm to a database of document types, and identifying a match between the extrapolated information a document type. The computing device applies an OCR algorithm that corresponds to the determined document type to a second portion of the electronic image, identifies items extracted from the second portion, determines that at least one identified item matches an original item entry, and marks each matching item. The computing device renders an updated listing of item entries on the graphical user interface with a listing of each non-matching item and a marked listing of each matching item.