A license plate detection and recognition system receives training data comprising images of license plates. The system prepares ground truth data from the training data based predefined parameters. The system trains a first machine learning algorithm based on the ground truth data to generate a license plate detection model. The license plate detection model is configured to detect one or more regions in the images. The one or more regions contains a candidate for a license plate. The LPDR system generates a bounding box for each region. The LPDR system trains a second machine learning algorithm based on the ground truth data and the license plate detection model to generate a license plate recognition model. The license plate recognition model generates a sequence of alphanumeric characters with a level of recognition confidence for the sequence.

Systems and methods for training machine learning models based on domain constraints are disclosed. An example method includes receiving a plurality of images, each image associated with a cluster of a plurality of clusters, the plurality of clusters representing an output of a second machine learning model, assigning a label to each cluster of the plurality of clusters based at least in part on a plurality of constraints, identifying, based at least in part on the plurality of constraints, a first label mismatch for a first image, the first label mismatch indicating that the first image belongs to a first cluster but should be assigned to a second cluster different from the first cluster, reassigning the first image to the second cluster, and training the first machine learning model, based on the labeled clusters of the plurality of clusters, to predict labels associated with subsequently received image data.

Systems and methods for extracting information from a sheet of material to facilitate organization of information from paper, and other physical writing surfaces are provided. An example system includes a sheet of material and a device for scanning the sheet with an optical sensor. The sheet of material includes an indication region. The indication region allows for indictors to be marked corresponding with at least one a corresponding subregion to be extracted. The sheet of material further includes at least one fiducial mark for identifying a boundary of the sheet. The device includes a processor operably coupled to the optical sensor for causing the optical sensor to scan the sheet and detect a boundary thereof using the fiducial marks and further identify a designated subregion of the sheet. Upon identification of the designated subregion, the processor is configured to extract information contained in the designated subregion for organization of information.

A license plate detection and recognition system receives training data comprising images of license plates. The system prepares ground truth data from the training data based predefined parameters. The system trains a first machine learning algorithm based on the ground truth data to generate a license plate detection model. The license plate detection model is configured to detect one or more regions in the images. The one or more regions contains a candidate for a license plate. The LPDR system generates a bounding box for each region. The LPDR system trains a second machine learning algorithm based on the ground truth data and the license plate detection model to generate a license plate recognition model. The license plate recognition model generates a sequence of alphanumeric characters with a level of recognition confidence for the sequence.

A method includes receiving a binary annotation of source text; performing a close operation on the binary annotation to generate a closed annotation using an initial kernel size; defining one or more contours in the closed annotation using one or more bounding boxes, respectively; determining a subset of the one or more contours for which a percentage of area occupied by text within a corresponding bounding box exceeds a threshold; and generating a final annotation of the source text based on the subset of the one or more contours.

A method and an apparatus for generating an image are provided. The method includes: acquiring a screenshot of a webpage preloaded by a terminal as a source image; recognizing connection areas in the source image, and generating first circumscribed rectangular frames outside outlines of the connection areas; combining, in response to determining that a distance between the connection areas is smaller than a preset distance threshold, the connection areas, and generating a second circumscribed rectangular frame outside outlines of the combined connection areas; and generating, based on a nested relationship between the first circumscribed rectangular frames and the second circumscribed rectangular frames and pictures in the first circumscribed rectangular frames, a target image. The first circumscribed rectangular frames and the second circumscribed rectangular frame are respectively generated by recognizing and combining the connection areas in the source image.

A method for outputting a drawing reference number description regarding a patent drawing reference number according to an embodiment of the present disclosure may include recognizing a size of a patent drawing and a position of a drawing reference number included in the patent drawing and acquiring a relative position coordinate of the drawing reference number in the patent drawing; setting a relative position coordinate of the drawing reference number description corresponding to the drawing reference number based on the acquired relative position coordinate; and outputting the drawing reference number description on the set relative position coordinate, thereby outputting such that the drawing reference number description corresponds to the drawing reference number.

A handwritten content removing method and device and a storage medium. The handwritten content removing method comprises: acquiring an input image of a text page to be processed, the input image comprising a handwritten region, which comprises a handwritten content (S10); identifying the input image so as to determine the handwritten content in the handwritten region (S11); and removing the handwritten content in the input image so as to obtain an output image (S12).

A text recognition method and apparatus that relate to the field of information processing technologies are provided. This effectively resolves a low recognition rate of curved text. The text recognition method includes: obtaining a to-be-detected image; determining a target text detection area in the to-be-detected image, where the target text detection area includes target text in the to-be-detected image, and the target text detection area is a polygonal area including m (m is a positive integer greater than 2) vertex pairs; correcting the polygonal area to m−1 rectangular areas to obtain a corrected target text detection area; and performing text recognition on the corrected target text detection area, and outputting the target text.

Computerized techniques for improved binarization and extraction of information from digital image data are disclosed in accordance with various embodiments. The inventive concepts include rendering a digital image using a plurality of binarization thresholds to generate a plurality of binarized digital images, wherein at least some of the binarized digital images are generated using one or more binarization thresholds that are determined based on a priori knowledge regarding an object depicted in the digital image; identifying one or more connected components within the plurality of binarized digital images; and identifying one or more text regions within the digital image based on some or all of the connected components. Systems and computer program products are also disclosed.