Abstract: The disclosure relates to KRASG12D inhibitor compounds having the structure of Formula (A) or Formula (B), pharmaceutical compositions thereof, and methods of use thereof for inhibiting, treating, and/or preventing KRASG12D mutation-associated diseases, disorders and conditions.

Abstract: An interaction method, an electronic device and a computer storage medium. The interaction method includes: acquiring a trajectory point set input by a user, wherein the trajectory point set at least includes one trajectory point; determining a candidate object according to the trajectory point set, wherein the candidate object includes a first candidate object and a second candidate object; displaying a first selection icon for representing the first candidate object and a second selection icon for representing the second candidate object; and displaying the first candidate object or executing an operation corresponding to the first candidate object in response to the user's selection operation on the first selection icon, and displaying the second candidate object or executing an operation corresponding to the second candidate object in response to the user's selection operation on the second selection icon.

Abstract: A method and apparatus for recognizing an imaged information-bearing medium, a computer-readable storage device and a computer device are provided. The method comprising: acquiring a first image of the imaged information-bearing medium; performing text recognition on the first image to acquire a text content of the imaged information-bearing medium; classifying the imaged information-bearing medium to acquire a type of the imaged information-bearing medium; and archiving the text content according to the type.

Abstract: A character recognition method includes: performing feature extraction on an image to be recognized to obtain a first feature map; processing the first feature map to at least obtain N first candidate carrier detection boxes, each first candidate carrier detection box being configured to outline a region of a character carrier; screening the N first candidate carrier detection boxes to obtain K first target carrier detection boxes; performing a feature extraction on the first feature map to obtain a second feature map; processing the second feature map to obtain L first candidate character detection boxes, each first candidate character detection box being configured to outline a region containing at least one character; screening the L first candidate character detection boxes to obtain J first target character detection boxes; and recognizing characters in the J first target character detection boxes to obtain J target character informations.

Abstract: The present disclosure relates to a handwritten text recognition method, including: acquiring an information sequence including a plurality of track points of handwritten text, wherein information on each track point comprises its abscissa, writing time and writing state value; dividing the plurality of track points into a plurality of strokes according to the writing state value of each track point, the writing state value including a first value representative of stroke pen-up and a second value representative of stroke pen-down, respectively; calculating a first segmentation threshold of the handwritten text; determining a first text segmentation point according to a result of comparison between an absolute value of a difference between abscissas of a start track point of one stroke and an end track point of its previous stroke and the first segmentation threshold; and performing text segmentation according to the first text segmentation point to obtain a text segmentation result.

Abstract: The present disclosure provides an answer evaluation method, an answer evaluation system, an electronic device, and a medium. The method comprises: acquiring an answer image, for a test paper answered by a use; classifying the answer image based on a pre-trained test question classification model, so as to obtain an objective question answer area and a subjective question answer area; identifying at least one objective question in the objective question answer area and an objective question answers for each of the at least one objective question; identifying at least one subjective question in the subjective question answer area and a subjective question answers for each of the at least one subjective question; and determining a total score value of the test paper based on the objective question, the objective question answer, the subjective question and the subjective question answer.

Abstract: The present invention provides a word recognition method. The method includes: acquiring an image of a word to be recognized; recognizing edges of each character of the word to be recognized from the image of the word to be recognized; determining a geometric position of the word to be recognized; stretching the geometric position of the word to be recognized to a horizontal position; and recognizing the word to be recognized in the horizontal position.

Abstract: A text recognition method and apparatus, a storage medium, and an electronic device. Said method comprises: acquiring a text image (S101); determining at least one text box from the text image, each text box corresponding to at least one word (S102); determining, from the at least one text box, a text box to be recognized (S103); determining, from the text image, a picture unit corresponding to the text box to be recognized (S104); rotating the picture unit to a target pose (S105); and performing text recognition on the picture unit in the target pose, and acquiring a target recognition result (S106). The method, apparatus, storage medium, and electronic device can recognize a text unit in a text having a large inclination angle.

Abstract: The present disclosure relates to a handwritten text recognition method, including: acquiring an information sequence including a plurality of track points of handwritten text, wherein information on each track point comprises its abscissa, writing time and writing state value; dividing the plurality of track points into a plurality of strokes according to the writing state value of each track point, the writing state value including a first value representative of stroke pen-up and a second value representative of stroke pen-down, respectively; calculating a first segmentation threshold of the handwritten text; determining a first text segmentation point according to a result of comparison between an absolute value of a difference between abscissas of a start track point of one stroke and an end track point of its previous stroke and the first segmentation threshold; and performing text segmentation according to the first text segmentation point to obtain a text segmentation result.

Abstract: A character recognition method includes: performing feature extraction on an image to be recognized to obtain a first feature map; processing the first feature map to at least obtain N first candidate carrier detection boxes, each first candidate carrier detection box being configured to outline a region of a character carrier; screening the N first candidate carrier detection boxes to obtain K first target carrier detection boxes; performing a feature extraction on the first feature map to obtain a second feature map; processing the second feature map to obtain L first candidate character detection boxes, each first candidate character detection box being configured to outline a region containing at least one character; screening the L first candidate character detection boxes to obtain J first target character detection boxes; and recognizing characters in the J first target character detection boxes to obtain J target character informations.

Abstract: The present invention provides a word recognition method. The method includes: acquiring an image of a word to be recognized; recognizing edges of each character of the word to be recognized from the image of the word to be recognized; determining a geometric position of the word to be recognized; stretching the geometric position of the word to be recognized to a horizontal position; and recognizing the word to be recognized in the horizontal position.

Abstract: A method and apparatus for recognizing an imaged information-bearing medium, a computer-readable storage device and a computer device are provided. The method comprising: acquiring a first image of the imaged information-bearing medium; performing text recognition on the first image to acquire a text content of the imaged information-bearing medium; classifying the imaged information-bearing medium to acquire a type of the imaged information-bearing medium; and archiving the text content according to the type.

Abstract: The present disclosure provides a training method for a tag identification network, a tag identification apparatus/method and device. The training method for a tag identification network includes: acquiring a first number of first images, each of the first images having its standard tag; adjusting parameters of the tag identification network by utilizing the first number of first images and their standard tags; selecting a second number of first images from the first number of first images as images to be converted, the second number being smaller than the first number; performing a style conversion process on each of images to be converted to generate a second image corresponding thereto, and serving a standard tag of the image to be converted as a standard tag of the second image; and adjusting the parameters of the tag identification network by utilizing the second number of the second images and their standard tags.

Abstract: The present disclosure provides an answer evaluation method, an answer evaluation system, an electronic device, and a medium. The method comprises: acquiring an answer image, for a test paper answered by a use; classifying the answer image based on a pre-trained test question classification model, so as to obtain an objective question answer area and a subjective question answer area; identifying at least one objective question in the objective question answer area and an objective question answers for each of the at least one objective question; identifying at least one subjective question in the subjective question answer area and a subjective question answers for each of the at least one subjective question; and determining a total score value of the test paper based on the objective question, the objective question answer, the subjective question and the subjective question answer.

Abstract: The present disclosure provides a training method for a tag identification network, a tag identification apparatus/method and device. The training method for a tag identification network includes: acquiring a first number of first images, each of the first images having its standard tag; adjusting parameters of the tag identification network by utilizing the first number of first images and their standard tags; selecting a second number of first images from the first number of first images as images to be converted, the second number being smaller than the first number; performing a style conversion process on each of images to be converted to generate a second image corresponding thereto, and serving a standard tag of the image to be converted as a standard tag of the second image; and adjusting the parameters of the tag identification network by utilizing the second number of the second images and their standard tags.

Abstract: A permanent magnet material and a method thereof. The permanent magnet material comprises one or more rare earth elements and one or more transition metal elements, wherein the atomic percentage of the one or more rare earth elements is less than or equal to 13%, and the permanent magnet material has a maximum magnetic energy product of greater than or equal to 18 MGOe.

Abstract: A permanent magnet material and a method thereof. The permanent magnet material comprises one or more rare earth elements and one or more transition metal elements, wherein the atomic percentage of the one or more rare earth elements is less than or equal to 13%, and the permanent magnet material has a maximum magnetic energy product of greater than or equal to 18 MGOe.