8results about How to "Solve the low recognition accuracy" patented technology

This application belongs to the field of character recognition, specifically relating to character recognition methods, devices, and storage media. The method includes: acquiring image data, which includes text information to be recognized; acquiring a pre-trained character recognition model, which includes a main branch and sub-branches, the sub-branch being used to acquire semantic information from the image data; the main branch being used to recognize the text information from the image data using the semantic information; and inputting the image data into the character recognition model to obtain the recognized text information. This method can solve the problem of low accuracy in traditional character recognition methods. Because the character recognition model can fully learn the semantic information of the text during the recognition process, it can improve the model's recognition accuracy, thereby increasing the overall accuracy of character recognition.

The application relates to a pedestrian re-identification method and belongs to the technical field of image recognition processing. The application proposes attribute-guided attention, provides attribute-related attention guidance for each attribute, performs feature extraction on a high-level feature map by using a convolution kernel with the same number of pedestrian attributes, obtains a feature map of each attribute, and thus improves the semantic representation effect of an attribute feature vector. Then, a dependency relationship between attributes is established according to the feature vector of each attribute, the recognition accuracy of one attribute is used to assist the recognition of another attribute through the dependency relationship, and the attribute recognition accuracy is improved as a whole.

The embodiment of the invention provides a method and device for recognizing vehicle information. The method comprises the steps that a first image, collected by a first camera, of a first vehicle parked on a parking space is acquired; under the condition that the vehicle information of the first vehicle cannot be recognized through the first image, determining a target vehicle matched with the first vehicle in a second image collected by a second camera for the lane according to the position of the first vehicle in the first image; and determining the vehicle information of the target vehicle identified by the second image as the vehicle information of the first vehicle. According to the invention, the problem of low vehicle information identification accuracy is solved.

The invention belongs to the technical field of image recognition and processing, and aims to provide an intelligent homework reviewing method and system, electronic equipment and a program product. According to the method, page structure adaptive recognition is realized through template matching, high-precision image correction is completed by using anchor geometric constraints, character recognition is performed through a single character recognition model, and objective question answering information is automatically scored. According to the invention, the problem of low recognition accuracy of subjective questions and complex answering scenes caused by recognition of handwritten texts of students depending on OCR in the prior art is solved, and the problem of low recognition accuracy of OCR caused by poor handwriting of students in the prior art is effectively solved; in addition, full-process high-precision intelligent reviewing from image acquisition, area positioning, character recognition to automatic objective question scoring and subjective question scoring summarization is realized, and homework reviewing efficiency and accuracy are greatly improved.

The invention provides a three-dimensional perception intention recognition method, and relates to the technical field of intention recognition, and the method comprises the steps: obtaining the head posture data of a user in a three-dimensional space and the multi-modal information of the three-dimensional space through the three-dimensional space information, obtaining the sight direction of the user through an eye movement estimation model, and fusing the head posture data, thereby achieving the recognition of the three-dimensional perception intention. The method comprises the following steps: acquiring the sight line of a user in a three-dimensional space, fusing three-dimensional space information, acquiring candidate objects on the sight line of the user, fusing multi-modal information, acquiring an object interacted by a user intention from the candidate objects, and acquiring the user intention according to the object interacted by the user, so as to increase in an intention recognition process. The problem that existing intention recognition is low in accuracy is solved, and the method is suitable for intention recognition.

This invention discloses a natural language interaction method for a construction safety AI assistant, belonging to the field of artificial intelligence interaction technology. It includes: receiving voice or text interaction requests input by construction personnel, processing the voice or text interaction requests to obtain standardized text requests; based on a pre-trained natural language processing model, performing intent recognition and entity extraction on the standardized text requests, outputting intent-entity association pairs; constructing a construction safety professional knowledge base, retrieving from the corresponding knowledge base according to the intent-entity association pairs, and generating natural language response content that conforms to the understanding of construction personnel; feeding back the generated response content to the construction personnel in voice or text form, while simultaneously recording the intent type, entity information, and response satisfaction of this interaction. This invention can accurately understand construction personnel's requests for safety procedure inquiries, operating specification inquiries, and training examinations, providing immediate and accurate intelligent responses.

The embodiment of the present application provides a kind of based on deep learning's heat dissipation substrate defect detection method and system, it is related to the technical field of heat dissipation substrate defect detection technology.Its method includes: obtaining the three-dimensional point cloud data of the heat dissipation substrate to be detected;The three-dimensional point cloud data is preprocessed to generate standardized input point cloud;The geometric features of the input point cloud are extracted and fused by the pre-trained defect detection model at multiple scales to obtain defect class prediction results.Through the present application, the problem of low complex defect recognition accuracy is solved, and the effect of improving the complex defect recognition accuracy is achieved.

This application discloses a method, apparatus, computer-readable medium, and electronic device for document authenticity verification. The method includes: acquiring anti-counterfeiting verification results obtained by performing anti-counterfeiting verification processing on multiple document images of a target document using an anti-counterfeiting verification model; performing feature mining on the anti-counterfeiting verification results of the multiple document images to obtain extended features; generating at least one feature combination based on the anti-counterfeiting verification results and the extended features, and verifying each feature combination using a verification model corresponding to each feature combination to obtain a verification result corresponding to each feature combination; and determining the authenticity of the target document based on the verification results corresponding to each feature combination. The technical solution of this application can verify the authenticity of a target document from multiple dimensions, improving the accuracy of document authenticity verification.