A method for recognizing gesture strokes in user input, comprising: receiving data generated based on the user input, the data representing a stroke and comprising a plurality of ink points in a rectangular coordinate space and a plurality of timestamps associated respectively with the plurality of ink points; segmenting the plurality of ink points into a plurality of segments each corresponding to a respective sub-stroke of the stroke and comprising a respective subset of the plurality of ink points; generating a plurality of feature vectors based respectively on the plurality of segments; and applying the plurality of feature vectors as an input sequence representing the stroke to a trained stroke classifier to generate a vector of probabilities including a probability that the stroke is a non-gesture stroke and a probability that the stroke is a given gesture stroke of a set of gesture strokes.

A handwriting recognition method and apparatus, and an electronic device and a storage medium are provided. The method includes: acquiring a text image containing handwritten text; inputting the text image into a convolutional neural network, and extracting a CNN feature and a HOG feature of the text image; and extracting the handwritten text in text image according to the CNN feature and the HOG feature.

Systems and methods for monitoring a dental patient's progress during treatment. A first teeth mask for a captured 2D image of teeth at a particular time during treatment and a second teeth mask for an expected 2D image of the teeth may be generated. The expected 2D image may be generated from an expected 3D model representing an expected configuration of the teeth at the particular time. The captured 2D image and the expected 2D image may be compared, with the first and second teeth masks aligned, to determine whether the teeth are within a threshold level of correspondence to the expected configuration. An indication as to whether the dental treatment is proceeding as expected based on whether the configuration of the teeth is within the threshold level of correspondence may be provided.

A method for generating a target function is provided. The method includes: performing normalization processing on a vector corresponding to each pixel in a target feature map set to generate a target vector, so as to obtain a target vector set; generating hash coding corresponding to each vector in the target vector set, to obtain a hash coding set; determining a prior probability of each hash coding in the hash coding set; and generating a target function based on an entropy of the prior probability.

A method for generating a target function is provided. The method includes: performing normalization processing on a vector corresponding to each pixel in a target feature map set to generate a target vector, so as to obtain a target vector set; generating hash coding corresponding to each vector in the target vector set, to obtain a hash coding set; determining a prior probability of each hash coding in the hash coding set; and generating a target function based on an entropy of the prior probability.

A character segmentation method and apparatus, and a computer-readable storage medium are provided. The method includes converting a character area image into a grayscale image; converting the grayscale image into an edge binary image by edge detection; acquiring character box segmentation blocks from the edge binary image by projection; and determining a target character area from the character box segmentation blocks by contour detection, and performing character segmentation on the character area image according to the target character area; or comprises: converting a character area image into a grayscale image; performing clustering analysis on the grayscale image by fuzzy C-means clustering, and binarizing the grayscale image according to the analysis result; acquiring character positioning blocks from a binary image by projection; and performing character segmentation on the character area image according to position information of the character positioning blocks. Character segmentation can be performed on a relatively low quality image.

A character segmentation method and apparatus, and a computer-readable storage medium are provided. The method includes converting a character area image into a grayscale image; converting the grayscale image into an edge binary image by edge detection; acquiring character box segmentation blocks from the edge binary image by projection; and determining a target character area from the character box segmentation blocks by contour detection, and performing character segmentation on the character area image according to the target character area; or comprises: converting a character area image into a grayscale image; performing clustering analysis on the grayscale image by fuzzy C-means clustering, and binarizing the grayscale image according to the analysis result; acquiring character positioning blocks from a binary image by projection; and performing character segmentation on the character area image according to position information of the character positioning blocks. Character segmentation can be performed on a relatively low quality image.

Dental treatment monitoring systems and methods may include accessing an input image of teeth taken at a particular time during dental treatment, and determining virtual-camera parameters that represent an estimated position and orientation of a virtual camera for producing a generated image from a time-projected 3D model of the teeth. The virtual-camera parameters may be iteratively adjusted by: generating a first generated image by modifying the virtual-camera parameters based on a first jaw in the generated image; determining a pixel-associated cost based on a comparison of the first generated image to the input image; generating a second generated image by modifying the first virtual-camera parameters based a second jaw in the first generated image; and determining a pixel-associated cost based on a comparison of the second generated image and the input image. The generated image may be generated from the time-projected 3D model using the adjusted virtual-camera parameters.

Provided are a computer program product, system, and method for training and using a vector encoder to determine vectors for sub-images of text in an image to subject to optical character recognition. A vector encoder is trained to encode images representing text into vectors in a vector space. Vectors of images representing similar text have a high degree of cohesion in the vector space. Vectors of images representing dissimilar text have a low degree of cohesion in the vector space. An input image is processed to determine sub-images of the input image that bound text represented in the input image. The sub-images are inputted to the vector encoder to output sub-image vectors. The vector encoder generates a search vector for search text. Optical character recognition is applied to at least one region of the input image including the sub-images having sub-image vectors matching the search vector.

A method for recognizing gesture strokes in user input, comprising: receiving data generated based on the user input, the data representing a stroke and comprising a plurality of ink points in a rectangular coordinate space and a plurality of timestamps associated respectively with the plurality of ink points; segmenting the plurality of ink points into a plurality of segments each corresponding to a respective sub-stroke of the stroke and comprising a respective subset of the plurality of ink points; generating a plurality of feature vectors based respectively on the plurality of segments; and applying the plurality of feature vectors as an input sequence representing the stroke to a trained stroke classifier to generate a vector of probabilities including a probability that the stroke is a non-gesture stroke and a probability that the stroke is a given gesture stroke of a set of gesture strokes.