Aspects of the disclosure provide for systems and processes for assessing image quality for optical character recognition (OCR), including but not limited to: segmenting an image into patches, providing the segmented image as an input into a first machine learning model (MLM), obtaining, using the first MLM, for each patch, first feature vectors representative of a reduction of imaging quality in a respective patch, and second feature vectors representative of a text content of the respective patch, providing to a second MLM the first feature vectors and the second feature vectors, and obtaining, using the second MLM, an indication of suitability of the image for OCR.

Automated processing, enforcement and intelligent management of vehicle operation violations is disclosed. A method, that is also disclosed, includes obtaining at least one image within which is shown at least a portion of a vehicle. The method also includes receiving image data for the at least one image, and analyzing the image data to generate a violation score for each of one or more potential vehicle operation violations for the vehicle. When the violation score is in-between an upper violation threshold and a lower consideration threshold, user input is obtained that either affirms or rejects existence of a violation and then, once the user input is obtained, a violation notification is generated only when the user input affirms the existence of the violation. When the violation score is higher than the violation threshold, the violation notification is generated without the user input being obtained.

Aspects of the disclosure can provide method for identifying a face where multiple images to be identified are received. Each of the multiple images includes a face image part. Each face image of face images in the multiple images to be identified is extracted. An initial figure identification result of identifying a figure in the each face image is determined by matching a face in the each face image respectively to a face in a target image in an image identification library. The face images are grouped. A target figure identification result for each face image in each group is determined according to the initial figure identification result for the each face image in the each group.

An image correction method and a processor are disclosed. The method includes performing a feature point search on a quick response (QR) code image to determine multiple feature points, dividing a coded area of the QR code image into multiple sub-regions according to the multiple feature points, determining a compensation vector for each sub-region according to the feature points corresponding to each sub-region, and compensating and correcting each sub-region according to the compensation vector of each sub-region to obtain a corrected image. Thus, the solution provided by the present application can avoid interference between different sub-regions by means of correcting the QR code image in a regional manner using the compensation vectors, thereby more accurately correcting the distortion of the QR code image.

An image correction method and a processor are disclosed. The method includes performing a feature point search on a quick response (QR) code image to determine multiple feature points, dividing a coded area of the QR code image into multiple sub-regions according to the multiple feature points, determining a compensation vector for each sub-region according to the feature points corresponding to each sub-region, and compensating and correcting each sub-region according to the compensation vector of each sub-region to obtain a corrected image. Thus, the solution provided by the present application can avoid interference between different sub-regions by means of correcting the QR code image in a regional manner using the compensation vectors, thereby more accurately correcting the distortion of the QR code image.

An image classification model training method and apparatus are provided. Classification results of each image outputted by an image classification model are obtained. When the classification results outputted by the image classification model do not meet a reference condition, a reference classification result is constructed based on the classification results outputted by the image classification model. Because the reference classification result can indicate a probability that images belong to each class, a parameter of the image classification model is updated to obtain a trained image classification model based on a total error value between the classification results of the each image and the reference classification result.

An image classification model training method and apparatus are provided. Classification results of each image outputted by an image classification model are obtained. When the classification results outputted by the image classification model do not meet a reference condition, a reference classification result is constructed based on the classification results outputted by the image classification model. Because the reference classification result can indicate a probability that images belong to each class, a parameter of the image classification model is updated to obtain a trained image classification model based on a total error value between the classification results of the each image and the reference classification result.

Systems and methods for acquiring and inspecting lens images of ophthalmic lenses using one or more cameras to acquire the images of the lenses in a dry state or a wet state. The images are preprocessed and then inputted into an artificial intelligence network, such as a convolutional neural network (CNN), to analyze and characterize for type of lens defects. The artificial intelligence network identifies defect regions on the images and output defect categories or classifications for each of the images based in part on the defect regions.

An image detection device and an image detection method are provided. The image detection method includes: obtaining an image, where the image includes an object; adjusting a first size of the image to generate an adjusted image; generating a first divided image and a second divided image according to the image; and detecting the object in the image based on a plurality of input images, where the plurality of input images includes the first divided image, the second divided image, and the adjusted image.

An object recognition method is performed at an electronic device. The method includes: pre-processing a target image, to obtain a pre-processed image, the pre-processed image including three-dimensional image information of a target region of a to-be-detected object, processing the pre-processed image by using a target data model, to obtain a target probability, the target probability being used for representing a probability that an abnormality appears in a target object in the target region of the to-be-detected object; and determining a recognition result of the target region of the to-be-detected object according to the target probability, the recognition result being used for indicating the probability that the abnormality appears in the target region of the to-be-detected object. The object recognition method can effectively improve accuracy of object recognition and avoid a case of incorrect recognition.