A serial number recognition parameter determination apparatus includes: a generation unit, an identification unit, and an evaluation index calculation unit. The generation unit generates a parameter set of a program, the program being used when a paper sheet handing apparatus identifies, from an image of a paper sheet, character present regions in which characters that form a serial number are present. The identification unit identifies, from an image of the paper sheet, the character present regions by using the parameter set that is generated by the generation unit. The evaluation index calculation unit calculates an evaluation index of the parameter set based on the character present regions that are identified by the identification unit.

Systems and methods for obscuring images and/or text during a screen sharing operation in a video conferencing session are described herein. In some embodiments, a client device detects a screen sharing operation. As part of the screen sharing operation, the client device captures an image of a display. The client device recognizes images and/or text in the image of the display and determines whether any of the images and/or text are restricted. If the images and/or text are determined to be restricted, the client device obscures the images and/or text prior to encoding of the image of the display for transmission.

Methods and systems for providing notifications in an extended reality (XR) environment are described herein. A computing device may provide, to a user and via an XR device, an XR environment. The computing device may detect one or more first locations of one or more display devices. At least one first display device of the one or more display devices may be in a physical environment around the XR device. The computing device may retrieve one or more notifications for display in the XR environment and determine, based on the one or more first locations of the one or more display devices, one or more second locations for the one or more notifications. The computing device may then provide, in the XR environment and at the one or more second locations, the one or more notifications.

Systems and methods for performing OCR of a series of images depicting text symbols. An example method comprises: receiving a current image of a series of images of an original document, wherein the current image at least partially overlaps with a previous image of the series of images; performing optical symbol recognition (OCR) of the current image to produce an OCR text and a corresponding text layout; identifying, using the OCR text and the corresponding text layout, a plurality of textual artifacts in each of the current image and the previous image, wherein each textual artifact is represented by a sequence of symbols that has a frequency of occurrence within the OCR text falling below a threshold frequency; identifying, in each of the current image and the previous image, a corresponding plurality of base points, wherein each base point is associated with at least one textural artifact of the plurality of textual artifacts; identifying, using coordinates of matching base points in the current image and the previous image, parameters of a coordinate transformation converting coordinates of the previous image into coordinates of the current image; associating, using the coordinate transformation, at least part of the OCR text with a cluster of a plurality of clusters of symbol sequences, wherein the OCR text is produced by processing the current image and wherein the symbol sequences are produced by processing one or more previously received images of the series of images; identifying an order of clusters of symbol sequences, the order reflecting a layout of the original document; and producing, in view of the order of clusters, a resulting OCR text representing at least a portion of the original document.

A cascade auto-review system for automated classification and annotation of input is provided. An example system is structure adaptive and task oriented and includes a communication module configured to receive the input including images, videos, and metadata. The system further includes a plurality of subsystems. Each subsystem has a series of successive classifier stages configured to detect tags in the input and approve or reject the tags based on the images, the videos, and the metadata. The system further includes a database to store results of the classification and annotation. The results are used to train computer vision and machine learning algorithms.

A computer implemented method for generating a region of interest on a digital image of a sidewall of a tyre, the sidewall having one or more embossed and/or engraved markings, is provided. The method comprises generating a histogram of oriented gradients feature map of the digital image, inputting the histogram of oriented gradients feature map into a trained convolutional neural network, wherein said trained convolutional neural network is configured to output a first probability based on the input histogram of oriented gradients feature map that a region of pixels of the digital image contains the embossed and/or engraved markings, and if the first probability is at or above a first predetermined threshold, accepting said region of pixels as said region of interest.

Disclosed are systems, methods and computer program products for using prior frame data for OCR processing of frames in video sources to detect natural language text therein. An example includes receiving a frame from a video source and retrieving prior frame data associated with the video source. The OCR-processing includes using prior frame data to detect blobs similar to blobs described in the prior frame data; using detected similar blobs to detect in the frame character candidates similar to character candidates described in the prior frame data; using detected similar character candidates to detect in the frame text candidates similar to text candidates described in the prior frame data; and using detected similar text candidates to detect in the frame text strings similar to text strings described in the prior frame data.

A computer implemented method is proposed for classifying one or more embossed and/or engraved markings on a sidewall of a tyre into one or more classes comprising digital image data of the sidewall of the tyre. The method comprises generating a first image channel from a first portion of the digital image data relating to a corresponding first portion of the sidewall of the tyre. Generating the first image channel comprises performing histogram equalisation on the first portion of the digital image data to generate the first image channel. The method further comprises generating a first feature map using the first image channel and applying a first classifier to the first feature map to classify said embossed and/or engraved markings into one or more first classes.

Methods and apparatus to determine the dimensions of a region of interest of a target object and a class of the target object from an image using target object landmarks are disclosed herein. An example method includes identifying a landmark of a target object in an image based on a match between the landmark and a template landmark; classifying a target object based on the identified landmark; projecting dimensions of the template landmark based on a location of the landmark in the image; and determining a region of interest based on the projected dimensions, the region of interest corresponding to text printed on the target object.

In the case where a user extracts a desired character string by specifying a range by using a finger or the like of him/herself on an image including a character, a specific character (space or the like) located at a position adjacent to the desired character string is prevented from being included unintendedly in the selected range. The character area corresponding to each character included in the image is identified and character recognition processing is performed for each of the identified character areas. Then, from results of the character recognition processing, a specific character is determined and the character area corresponding to the determined specific character is extended. Then, the range selected by the user in the displayed image is acquired and character recognition results corresponding to a plurality of character areas included in the selected range are output.