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.

An information processing apparatus (10) includes a controller (11) that acquires an image containing a figure and a character string and generates association information indicating an association between the figure and the character string based on a positional relationship between the figure and the character string in the image.

A character recognizing apparatus includes an acquiring unit, an identifying unit, and a character recognizing unit. The acquiring unit acquires a string image that is an image of a string generated in accordance with one of multiple string generation schemes. The identifying unit identifies a range specified for a result of character recognition in each of the multiple string generation schemes. The character recognizing unit performs first character recognition on the string image, and if a result of the first character recognition has a feature of a particular string generation scheme of the multiple string generation schemes, the character recognizing unit performs second character recognition on the string image within the range specified for a result of character recognition in the particular string generation scheme.

An image processing apparatus includes circuitry to set first upper limit values for vertical and horizontal sizes of a character included in image data for erecting direction determination, segment the image data in units of character into a plurality of rectangular areas, determine, in the image data, a plurality of first rectangular areas each of which satisfies the first upper limit values, perform character recognition on characters in the plurality of first rectangular areas in four directions of a +X direction, a −X direction, a +Y direction, and a −Y direction, calculate degrees of certainty of the four directions, determine whether a direction having a highest degree of certainty among the calculated degrees of certainty of the four directions is an erecting direction of the image data to output a determination result, and perform, along the erecting direction, character recognition on characters in a plurality of second rectangular areas of the image data, the plurality of second rectangular areas satisfying second upper limit values for the vertical and horizontal sizes smaller than the first upper limit values for erecting direction determination.

An identification card authenticity determining method based on deep learning according to the disclosure for automatically checking authenticity of an identification card includes: inputting identification card data to a feature information extraction model to extract pieces of feature information, expressing an indicator for checking authenticity of the identification card, from the identification card data; inputting the extracted pieces of feature information to a classification model to determine authenticity of the identification card; and when it is determined that the identification card is falsified, extracting a class activation map, where a falsification region of the identification card data is activated, from the pieces of feature information.

Character recognition processing suitable to a handwritten character area and a printed character area among character areas in a scanned image of a document is performed. Next, character recognition results for the handwritten character area and character recognition results for the printed character area are integrated and a likelihood indicating a probability of being an extraction target is calculated for a candidate character string that is an extraction candidate among the integrated character recognition results and a character string that is the item value is determined. Then, at the time of the determination, different evaluation indications are used in a case where a character originating from the handwritten character area is included in characters constituting the candidate character string and in a case where such a character is not included.

The technology described herein provides a handwritten postage that comprises handwriting on a postal item that forms a unique identifier for the postal item (e.g., envelope, postcard, sticker) when analyzed by a computer vision application. The unique identifier is computer derived from the handwritten postage and allows one instance of handwritten postage to be differentiated from all other instances of handwritten postage. The unique identifier may be derived from an image of an envelope that includes an instance of handwritten postage when the handwritten postage is activated. The unique identifier may be formed from a combination of handwriting content (e.g., to and from address), metadata (e.g., date activated), pre-printed content on the postal item (e.g., fiducial marks), post-printed content (e.g., to or from address) and the visual image created by all or a portion of the handwriting. Postage value is added to the handwritten postage through an activation process.

Methods and systems are provided to manage documents and extract information from documents by defining segments in each document, each of which is assigned a location in a coordinate system defined over a collection of documents. Metadata is attached to each segment to describe the contents, position, and semantic meaning of material within the segment. A segmenting-specific query language can be used to query the segments and respond to requests for information contained in the documents.

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.

A method comprising: receiving, from a user device at a first location, first image information of a surrounding environment of the user device; determining further image information of the surrounding environment of the user device at the first location, wherein the further image information is required for generating a spatial anchor in the model, wherein the spatial anchor point links the first real-world location of the user device to a corresponding location in the model; providing guidance information to the user device for capturing the further image information; receiving the further image information from the user device; and generating a spatial anchor point in the model based on the first image information and the further image information; wherein following generation of the spatial anchor point, the spatial anchor point is discoverable to one or more users of the model to provide information of the first location.