The present disclosure relates to document quality-based image pre-processing that measures, for each item, the quality of an input document image that is to be analyzed and omits a part or the entirety of a pre-processing process, or performs an image pre-processing process to which an appropriate algorithm is applied, so that an unnecessary operation may be decreased and a processing time may be reduced, and a higher character recognition rate may be obtained than an image pre-processing process uniformly applied. A document quality-based image pre-processing method according to an embodiment of the present disclosure may be a document quality-based image pre-processing method performed by a processor in an apparatus, the method including measuring a document quality of an input document image, classifying the document quality, and performing image pre-processing corresponding to the document quality classified for the document image.

A method for recognizing text includes: obtaining a first feature map of an image; for each target feature unit, performing a feature enhancement process on a plurality of feature values of the target feature unit respectively based on the plurality of feature values of the target feature unit, in which the target feature unit is a feature unit in the first feature map along a feature enhancement direction; and performing a text recognition process on the image based on the first feature map after the feature enhancement process.

A method for recognizing text includes: obtaining a first feature map of an image; for each target feature unit, performing a feature enhancement process on a plurality of feature values of the target feature unit respectively based on the plurality of feature values of the target feature unit, in which the target feature unit is a feature unit in the first feature map along a feature enhancement direction; and performing a text recognition process on the image based on the first feature map after the feature enhancement process.

There is provided a method of extracting characters from a license plate of a vehicle performed by a license plate character extraction device. The method comprises: converting a input image obtained by capturing the license plate of the vehicle into a grayscale image; generating a converted image based on a result of comparing a value of at least one pixel included in the grayscale image with a first average of values of pixels adjacent to the at least one pixel; generating a refined image based on a result of comparing the converted image with a binarized image obtained by binarizing the converted image; and extracting characters included in the refined image.

There is provided a method of extracting characters from a license plate of a vehicle performed by a license plate character extraction device. The method comprises: converting a input image obtained by capturing the license plate of the vehicle into a grayscale image; generating a converted image based on a result of comparing a value of at least one pixel included in the grayscale image with a first average of values of pixels adjacent to the at least one pixel; generating a refined image based on a result of comparing the converted image with a binarized image obtained by binarizing the converted image; and extracting characters included in the refined image.

The present disclosure provides a vision processing and model training method, device, storage medium and program product. A specific implementation solution is as follows: establishing an image classification network with the same backbone network as the vision model, performing a self-monitoring training on the image classification network by using an unlabeled first data set; initializing a weight of a backbone network of the vision model according to a weight of a backbone network of the trained image classification network to obtain a pre-training model, the structure of the pre-training model being consistent with that of the vision model, and optimize the weight of the backbone network by using real data set in a current computer vision task scenario, so as to be more suitable for the current computer vision task; then, training the pre-training model by using a labeled second data set to obtain a trained vision model.

The present disclosure provides a vision processing and model training method, device, storage medium and program product. A specific implementation solution is as follows: establishing an image classification network with the same backbone network as the vision model, performing a self-monitoring training on the image classification network by using an unlabeled first data set; initializing a weight of a backbone network of the vision model according to a weight of a backbone network of the trained image classification network to obtain a pre-training model, the structure of the pre-training model being consistent with that of the vision model, and optimize the weight of the backbone network by using real data set in a current computer vision task scenario, so as to be more suitable for the current computer vision task; then, training the pre-training model by using a labeled second data set to obtain a trained vision model.

A method for generating training data for hand-printed text recognition includes obtaining a structured document, obtaining a set of hand-printed character images and database metadata from a database, generating a modified document page image, and outputting a training file. The structured document includes a document page image that includes text characters and document metadata that associates each of the text characters to a document character label. The database metadata associates each of the set of hand-printed character images to a database character label. The modified document page image is generated by iteratively processing each of the text characters. The iterative processing includes determining whether an individual text character should be replaced, selecting a replacement hand-printed character image from the set of hand-printed character images, scaling the replacement hand-printed character image, and inserting the replacement hand-printed character image into the modified document page image.

A digital camera processing system with software to manage taking photos with a digital camera. Camera software controls the digital camera. A downloaded software component controls the digital camera software and causes a handheld mobile device to perform operations. The operations may include instructing a user to have the digital camera take photos of a check: displaying an instruction on a display of the handheld mobile device to assist the user in having the digital camera take the photos; or assisting the user as to an orientation for taking the photos with the digital camera. The digital camera processing system may generate a log file including a bi-tonal image formatted as a TIFF image.

A digital camera processing system with software to manage taking photos with a digital camera. Camera software controls the digital camera. A downloaded software component controls the digital camera software and causes a handheld mobile device to perform operations. The operations may include instructing a user to have the digital camera take photos of a check: displaying an instruction on a display of the handheld mobile device to assist the user in having the digital camera take the photos; or assisting the user as to an orientation for taking the photos with the digital camera. The digital camera processing system may generate a log file including a bi-tonal image formatted as a TIFF image.