A method for extracting data from a single-layer raster navigational chart (RNC) comprising: using a computer vision algorithm to extract color, text and symbol data from the RNC, storing the color, text, and symbol data in a database, and building an RNC data vector based solely on the color, text, and symbol data of the RNC, wherein the RNC data vector identifies geographical features shown in the RNC and a location of the geographical features' corresponding pixels in the RNC; and drawing a region of interest on the navigational chart based on user input and the RNC data vector, wherein a perimeter of the region of interest is georeferenced with latitude and longitude information.

The present disclosure discloses a method and system for visual inspection of a target product. The method includes a) receiving an image associated with the target product; generating a plurality of region of interests (ROIs) associated with the image; identifying, based on the plurality of non-terminal ROIs, a first set of features and a second set of features associated with the image. The first set of features and the second set of features are indicative of one of a presence of defect within the image or an absence of defect within the image. The method also includes determining, based on the first set of features and the second set of features, a result of the visual inspection of the target product associated with the image. The result is a success result or a failure result.

A handwriting geometry recognition and calibration system by using neural network and mathematical feature includes: a pre-processor for pre-processing coordinate points of geometric figures from user's handwriting so as to get a plurality of sample points which expresses the geometric figures to be recognized; a neural network connected to the pre-processor for receiving the sample points of the geometric figure and recognizing the geometric figure so as to acquire a coarse class of the geometric figure; and an mathematical logic unit connected to the neural network for receiving recognition results from the neural network, including coarse classifications which are used in a secondary classification by using conventional mathematical recognition logics so as to determine an exact geometry shape of the geometric figure; then the geometric figure being calibrated so as to get a geometry with a regular shape.

An apparatus includes circuitry; and a memory storing computer-executable instructions that cause the circuitry to execute acquiring training data, in which image data in a manual and text data in the manual are input data, and in which work procedure information is output data, the work procedure information being supplemented by adding, to the text data in the manual, text data that is generated based on the image data; performing machine learning by using the training data; and generating a machine learning model that outputs the work procedure information in response to receiving input of the image data in the manual and the text data in the manual.

A processor of an electronic device is configured to display a plurality of strokes in the display. The processor is configured to display, in response to a first input indicating selection of at least one character distinguished by the strokes, a first visual object for identifying a first sentence including the at least one character. The processor is configured to identify, in response to a second input indicating selection of the first visual object, strokes included in the first sentence among the strokes, based on a spacing between a first word including the at least one character and a second word, and moments of a plurality of words including the first word, and the second word are inputted. The processor is configured to display, in the display based on identification of the strokes included in the first sentence, a second visual object corresponding to the identified strokes among the strokes.

A training method for a video time-effectiveness classification model, performed by a computer device, includes obtaining video samples; extracting, from the video samples, sample image frames, first text information, and time-effectiveness sensitivity information; extracting image features from the sample image frames, text features from the first text information, and time-effectiveness sensitivity features of the time-effectiveness sensitivity information; and generating a trained video time-effectiveness classification model from an untrained neural network model by training the neural network model based on the image features, the text features, and the time-effectiveness sensitivity features, wherein the trained video time-effectiveness classification model may be configured to receive a video sample as input and output a predicted effective life cycle classification result.

A system and method of extracting tables and figures from a drawing document is disclosed. The method may include processing coloured image to segmented binary image and extracting a plurality of horizontal lines and a plurality of vertical lines from a foreground of the image. The method may further include detecting a set of candidate table region from the plurality of horizontal lines and the plurality of vertical lines in the image. Further, the method may include calculating textual region density corresponding to each of the set of candidate table regions in the image. The method may further include identifying at least one relevant table region from the set of candidate table regions in the image and a text free region from the at least one additional region in the image. The method may further include identifying at least one figure region from the dilated text free region.

A system for determining correction of handwriting Chinese characters includes a Chinese character collector for collecting template Chinese characters which is inputted by handwriting. A feature classifier is connected to a pre-processor for automatically classifying features of the template Chinese characters. Text features of the template Chinese characters are acquired by a program. A tested handwriting Chinese character collector is connected to the feature classifier for collecting the handwriting Chinese characters to be tested. Exact external rectangles and mass centers of the handwriting Chinese characters to be tested are calculated, which are inputted to the feature classifier and the text features for the handwriting Chinese characters to be tested are calculated. A feature comparator is connected to the feature classifier and serves to compare the text features of a handwriting Chinese character to be tested with feature sets stored in a feature set database.

At least one processor of an image processing system acquires a target object image including a target object. The at least one processor detects, from the target object image, a plurality of line segments located relatively on an outer side based on predetermined line segment detection processing. The at least one processor acquires, based on the plurality of line segments, information about an outline of the target object in the target object image. The at least one processor processes, based on the information, at least a part of the target object image so that the outline matches or approximates a predetermined outline.

A method can include performing optical character recognition on a document to define spatial locations of bounding boxes for characters, where each bounding box includes at least one character; identifying a spatial location of keyword characters via a corresponding one of the bounding boxes; applying an edge detection technique to generate a skeletonized version of the document; determining borders within the skeletonized version of the document to define regions; and extracting the characters within one of the regions that includes the keyword characters.