A wine label recognition method, a wine information management method and apparatus, a computer device, and a computer-readable storage medium are provided. The method includes: obtaining a wine image, and performing optical character recognition (OCR) on the wine image in a preset OCR manner, to obtain text included in the wine image (S21); performing deep learning recognition on the wine image in a preset deep learning recognition manner, to obtain an image feature included in the wine image (S22); and sifting out a target wine label matching the text and the image feature from a preset wine label database according to the text and the image feature, and using the target wine label as a wine label corresponding to the wine image (S33). Advantages of deep learning and OCR are fully utilized thereby improving accuracy and efficiency of wine label recognition and improving automation efficiency of wine information management.

This disclosure is directed to a method and system for matching human pose data in the form of 2D skeletons for the purposes of 3D reconstruction. The system may comprise a scoring module that assigns an affinity score to each pair of cross-view 2D skeletons, a matching module that assigns optimal pairwise matches based on the affinity scores, a grouping module that assigns each 2D skeleton to a group such that each group corresponds to a unique person, based on the pairwise matches; and a temporal consistency module that assigns each group an ID that maintains correspondence to the same person over the multi-video sequence.

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.

The disclosure herein describes providing signature data of an input document. Text data of the input document is obtained (e.g., OCR data generated from image data) and a first set of signature fields are identified using signature key-value pairs of the text data. A first subset of signed signature fields and a first subset of unsigned signature fields are determined based on mapping to a set of predicted values. A second set of signature fields are determined using a region prediction model applied to image data of the input document. Region images associated with the first subset of unsigned signature fields and with second set of signature fields are obtained and a second set of signed signature fields and a second set of unsigned signature fields are determined using a signature recognition model. Signature output data is provided including signed signature fields and/or unsigned signature fields.

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.

An information processing apparatus includes a receiver that receives an input image to be recognized and a processor configured to, by executing a program, align the input image with a template image in such a way as to match a recognition area of the input image and a recognition area defined on the template image, perform a process for recognizing the recognition area of the aligned input image, generate a check image including the template image and the aligned input image, and display the check image and a result of the process for recognizing the recognition area of the aligned input image such that a correspondence between the check image and the result is recognizable.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an active lighting system. In one aspect, a method includes receiving a first image of the physical document having a first glare signature and a second image of the physical document having a second glare signature that is different from the first glare signature; determining a first glare map of the first image and a second glare map of the second image; comparing the first glare map to the second glare map; and generating the digital image based on the comparison of the first and second glare maps.

A multifunctional fitness device comprising a fitness device body, a support arm and an intelligent control system, with a display device being disposed on a front surface of the fitness device body, the display device being a mirror display screen having functions of video teaching and ordinary mirror, a camera device being disposed at the top of the display device, and the camera device including a micro-camera and an infrared camera, support arms being disposed at both sides of the fitness device body through sliding rails respectively, a gear groove being disposed on the support arm, a gear fixing device for use in cooperation with the gear groove being disposed on the sliding rail, a rope being disposed inside the support arm, the other end of the rope being connected with an intelligent motor, which motor produces resistance to provide a resistant force to the support arm.

An image processing apparatus for setting a property of a document file by using a result of a character recognition process performed on a scanned image of a document is provided and includes an obtaining unit and an a setting unit. The obtaining unit obtains a character string by performing the character recognition process on a scanned image relating to a document file to be generated in this operation. The setting unit automatically sets the character string obtained by the obtaining unit as a character string to be used in a property of the document file to be generated in this operation if the character string obtained by the obtaining unit is a character string obtained in the character recognition process performed on a scanned image relating to a document file generated in the past and approved by a user a certain number of times or more.

This disclosure is directed to a method and system for matching human pose data in the form of 2D skeletons for the purposes of 3D reconstruction. The system may comprise a scoring module that assigns an affinity score to each pair of cross-view 2D skeletons, a matching module that assigns optimal pairwise matches based on the affinity scores, a grouping module that assigns each 2D skeleton to a group such that each group corresponds to a unique person, based on the pairwise matches; and a temporal consistency module that assigns each group an ID that maintains correspondence to the same person over the multi-video sequence.