Software User Assistance (UA) is afforded from captured User Interface (UI) screen images, with reference to persisted Machine Learning (ML) models. The captured screen images are processed—e.g., using rasterization, Optical Character Recognition (OCR), and/or establishment of a coordinate system—with individual UI elements being determined therefrom. Referencing the persisted ML models, the software application/application state for the captured image is identified. UA data relevant to that application/application state is generated from the model, and then provided to the user (e.g., in a text box overlying the UI screen). Through the capture and processing of UI screen images, embodiments afford a homogenous UA experience for installation, maintenance, and/or upgrade of heterogeneous members of a larger overall landscape, over software lifecycles. Embodiments may be deployed locally on a frontend computer, in order to avoid exporting UI images due to privacy and/or security concerns.

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.

The system is configured to create a generalized document automation framework that captures relevant data from documents based upon replicating historical human actions associated with a document. The system may use machine vision and natural language processing to match a new document to a document that was already human extracted in an existing corpus. This is accomplished by comparing both visual elements and textual elements. This match can be verified by statistical approaches by comparing the match metrics across multiple documents. After the match has been found and verified, the system then uses the historical extractions from the historical document and maps the extractions to similar regions in the new document based upon again both visual and text commonalities between documents. Data is then extracted from these regions of interest in the new document, sanity checked for data integrity against historical data, and then passed downstream for processing.

A system may include a processor configured to: obtain an image of an airport terminal chart; based on a latitudinal set of characters, determine a latitude for each line of latitude; based on the latitude for each line of latitude and a first image distance between the lines of latitude, determine a first ratio of latitudinal degrees between the lines of latitude to the first image distance; based on a longitudinal set of characters, determine a longitude for each line of longitude; based on the longitude for each line of longitude and a second image distance between the lines of longitude, determine a second ratio of longitudinal degrees between the lines of longitude to the second image distance; and output information associated with the first ratio, the second ratio, the determined latitude for each line of latitude, and the determined longitude for each line of longitude.

Disclosed herein are devices and methods for determining the identity of markings on tires. A portable tire scanner can comprise one or more light sources and detector that reflect light off tire markings and capture imagery of them. The scanner is operable to process the imagery to determine the identity of the markings. The marking can be the same color as the area of the tire around the marking (e.g., black-on-black) and the scanner can identify the marking by determining angular edges of the markings. Plural light sources and/or detectors can be used to provide plural perspectives to better determine the edges of the markings. The housing can have a form factor that allows the scanner to be hand-held, such that a user can aim the scanner at tires even while on a vehicle or in hard to reach positions. The scanner can be used to scan and identify several tire marking in succession.

Systems, devices, and techniques are disclosed for style-based dynamic content generation. A seed image, entity design data, entity style data, and text items may be received. Bounding boxes that identify areas of the seed image for the placement of the text items may be generated for the seed image. Variant images may be generated from the seed image, the entity design data, and the entity style data. The variant images may be generated by placing text items in the bounding boxes based on the entity design data and rendering text of the text items using the entity style data.

Systems and methods for authenticating a document are provided. The methods include receiving image data that includes a document and data from one or more sensors. The methods include determining a location of the document using the image data and the data received from the sensors. The methods further include determining electromagnetic signal parameters using the determined location of the document and emitting an electromagnetic signal at the document. The electromagnetic signal is based on the determined electromagnetic signal parameters. The methods include, while emitting the electromagnetic signal at the document, capturing a verification image of the document. The verification image includes security features that are exposed to the electromagnetic signal. The methods further include determining whether a security feature of the verification image meets security criteria and in accordance with a determination that the security feature of the verification image meets the security criteria, providing authentication information.

There is provided a solution for semantic representation of text in a document. In this solution, textual information comprising a sequence of text elements (220) and layout information (230) of the text element are determined from a document. The layout information (230) indicates a spatial arrangement of the plurality of text elements (220) presented within the document. Based at least in part on the plurality of text elements (220) and the layout information (230), respective semantic feature representations (180) of the plurality of text elements (220) are generated. By jointly using both the textual information and the layout information (230), rich semantics of the text elements (220) in the document can be effectively captured in the feature representations.

A method for operating a terminal, where the method includes performing by a processing unit of the terminal: extracting a character sequence from an optical tag scanned with a camera of the terminal, where the character sequence defines a service to be performed by a target application of the terminal; determining that the character sequence matches a target rule associated to the target application; and processing the character sequence so as to trigger the target application to perform the service.

Described embodiments relate to a method comprising: determining a candidate document comprising image data and character data and extracting the image data and the character data from the candidate document. The method comprises providing, to an image-based numerical representation generation model, the image data, and generating, by the image-based numerical representation generation model, an image-based numerical representation of the image data. The method comprises providing, to a character-based numerical representation generation model, the character data; and generating, by the character-based numerical representation generation model, a character-based numerical representation of the character data. The method comprises providing, to a consolidated image-character based numerical representation generation model, the image-based numerical representation and the character-based numerical representation; and generating, by the consolidated image-character based numerical representation generation model, a combined image-character based numerical representation of the candidate document.