A method in a mobile computing device includes: controlling a camera to capture an image; tracking, in association with the image, a pose of the mobile computing device in a coordinate system; detecting a region of interest (ROI) depicting an item in the image; determining a location of the ROI in the coordinate system, based on the tracked pose; obtaining an item identifier corresponding to the ROI by (i) when a previously recognized item identifier is not available, executing a recognition mechanism to derive the item identifier from the ROI, and (ii) when a previously recognized item identifier is available, bypassing the recognition mechanism and retrieving the previously recognized item identifier; and returning the obtained item identifier corresponding to the ROI.

Implementations of the present disclosure provide a solution for text line detection. In this solution, a first text region comprising a first portion of at least a first text element and a second text region comprising a second portion of at least a second text element are determined from an image. A first feature representation is extracted from the first text region and a second feature representation is extracted from the second text region. The first and second feature representations comprise at least one of an image eature representation or a semantic feature representation of the image. A link relationship between the first and second text regions can then be determined based at least in part on the first and second feature representations. The link relationship can indicate whether the first and second portions of the first and second text elements are located in a same text line. In this way, by detecting text regions and determining the link relationship thereof based on their feature representations, the accuracy and efficiency for detecting text lines in various images can be improved

A system and method of identifying objects is provided. In one aspect, the system and method includes a hand-held device with a display, camera and processor. As the camera captures images and displays them on the display, the processor compares the information retrieved in connection with one image with information retrieved in connection with subsequent images. The processor uses the result of such comparison to determine the object that is likely to be of greatest interest to the user. The display simultaneously displays the images the images as they are captured, the location of the object in an image, and information retrieved for the object.

A method for optical character recognition of text and information on a curved surface, comprising: activating an image capture device; scanning of the surface using the image capture device in order to acquire a plurality of scans of sections of the surface; performing OCR on the plurality of scans; separating the OCRed content into layers for each of the plurality of scans; merging the separated layers into single layers; and merging the single layers into an image.

In some implementations, a system may receive, from a camera, an image that depicts an object on a conveyor. The system may cause, based on an image processing model indicating that the image depicts the object, a robotic arm to attach to a label printer. The system may determine, using the image processing model, an object position of the object on the conveyor. The system may cause the robotic arm to move the label printer into an application position that corresponds to the object position on the conveyor. The system may cause the label printer to print a label. The system may cause the label printer to apply the label to the object.

A method for generating learning data for the neural network may comprise generating a license plate image by combining a background image, a frame image and a text image, generating a transformed image by performing at least one of a geometry transformation and a filter transformation on the license plate image, setting a text corresponding to the text image as target data for the transformed image, and generating the learning data including the transformed image and the target data.

A system, method, and computer program product provides a way to separate connected or adhered adjacent characters of a digital image for license plate recognition. As a threshold processing, the method performs a recognition of character adhesion by obtaining character parameters using an image processor. The parameters include a horizontal max crossing and a ratio of width and height. A first rule-based module is used responsive to the character parameters to distinguish the adhered characters (character adhesions) that are easy to judge, leaving the uncertain part to a character adhesion classifier model for discrimination. Character adhesion data is obtained by data augmentation including the adding of a random distance between two single characters to create class like adhered characters. Then the character adhesion classifier model of single character and character adhesion data is trained. Any uncertain part can be distinguished by the trained character adhesion classifier model.

The present disclosure relates to a battery test system for a vehicle that includes a camera configured to capture an image of a vehicle identification number located on the vehicle, the camera being coupled to a processor which determines characters of the vehicle identification number from the image of the camera and correlates the characters of the vehicle identification number to a vehicle identification number database to receive battery parameters for the vehicle, a battery tester that is removably connected to terminals of a battery of the vehicle and configured to receive battery test results, and a display which conveys information relating to the battery parameters and the battery test results.

A head-mounted device (HMD) can be configured to determine a request for recognizing at least one content item included within content framed within a display of the HMD. The HMD can be configured to initiate a head-tracking process that maintains a coordinate system with respect to the content, and a pointer-tracking process that tracks a pointer that is visible together with the content within the display. The HMD can be configured to capture a first image of the content and a second image of the content, the second image including the pointer. The HMD can be configured to map a location of the pointer within the second image to a corresponding image location within the first image, using the coordinate system, and provide the at least one content item from the corresponding image location.

Systems and methods of image searching include receiving content, receiving a request to select an image from content, selecting a plurality of items in the image, retrieving information about the selected item, and providing display data based on the retrieved information.