A text recognition method includes: acquiring an image including text information, the text information including M characters, M being a positive integer greater than 1; performing text recognition on the image to acquire character information about the M characters; recognizing reading direction information about each character in accordance with the character information about the M characters, the reading direction information being used to indicate a next character corresponding to a current character in a semantic reading order; and ranking the M characters in accordance with the reading direction information about the M characters to acquire a text recognition result of the text information.

A first decoder obtains a region including an object targeted for recognition based on a feature map obtained by performing convolutional processing on a processing target image. Next, the first decoder obtains, from the feature map, a partial feature map of a portion corresponding to the obtained region including the object targeted for recognition. Then, a second decoder obtains pixels corresponding to the object targeted for recognition based on the partial feature map. This reduces the amount of calculation required for decoder units included in a neural network.

A dental form image may be processed with a segmentation network to identify point labels corresponding to reference point labels of a reference form. The image and the point labels along with a reference image and the reference point labels may be processed by a pair of encoders to obtain offsets. Text blobs may be identified from portions of the image corresponding to the reference point labels, such as with correction according to the offsets. Image portions and text blobs for each field of the dental form may be processed to extract text. Intermediate values of machine learning models used to extract text may be input to a machine learning model estimating a procedure code for the dental form. Machine learning models may be used to correctly identify a provider referenced by the dental form.

Topological evolution of a lesion within a time series of medical imagery is provided. In various embodiments, a time series of medical images is read. Each of the images depicts a subject anatomy and a lesion. The lesion has a size and a contour within each of the medical images. At least one anatomical label is read for the subject anatomy within each of the plurality of images. Based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, a further contour of the lesion is predicted outside of the time series.

Systems and methods for detecting a hazard in a facility include the use of one or more cameras coupled with a hazard detection server. The hazard detection server is adapted to analyze images from the cameras, determine probabilities of hazards being present in the images, and provide an alert to a manager or workers when the probabilities exceed a hazard threshold.

Disclosed are methods for handwriting recognition. In some aspects, an image representing a page of a sample document is analyzed to identify a region having indications of handwriting. The region is analyzed to determine frequencies of a plurality of geometric features within the region. The frequencies may be compared to profiles or histograms of known language types, to determine if there are similarities between the frequencies in the sample document relative to those of the known language types. In some aspects, machine learning may be used to characterize the document as a particular language type based on the frequencies of the geometric features.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

This application discloses an image segmentation method in the field of artificial intelligence. The method includes: obtaining an input image and a processing requirement; performing multi-layer feature extraction on the input image to obtain a plurality of feature maps; downsampling the plurality of feature maps to obtain a plurality of feature maps with a reference resolution, where the reference resolution is less than a resolution of the input image; fusing the plurality of feature maps with the reference resolution to obtain at least one feature map group; upsampling the feature map group by using a transformation matrix W, to obtain a target feature map group; and performing target processing on the target feature map group based on the processing requirement to obtain a target image.

A process mining system performs process mining using visual logs generated from video streams of worker devices. Specifically, for a given worker device, the process mining system obtains a series of images capturing a screen of a worker device while the worker device processes one or more tasks related to an operation process. The process mining system determines activity labels for a plurality of images. An activity label for an image may indicate an activity performed on the worker device when the image was captured. The activity label is determined by extracting information from pixels of the image and inferring the activity of the worker device from the extracted information. The process mining system generates event logs from the visual logs of worker devices and uses the event logs for process mining.

A data capture component receives a video stream comprising a plurality of frames, wherein each frame comprises a data field. One or more text regions in a selected frame of the plurality of frames are identified. One of the one or more identified text regions that corresponds to a set of attributes associated with the data field are selected. The data of the one of the one or more identified text regions of the selected frame are compared with data of one or more text regions of a subsequent frame. Responsive to determining that the data of the one or more text regions of the subsequent frame is a closer match to the set of attributes, the data of the one of the one or more identified text regions of the selected frame are updated. The data of the one of the one or more identified text regions is then provided to a client device.