Examples of a computing device for text recognition is provided. The computing device comprises a processor coupled to a storage medium that stores instructions, which upon execution by the processor, cause the processor to receive a data file comprising an image, identify at least one contour in the image, partition the at least one contour into a plurality of segments, and identify a text character in each segment of the plurality of segments.

A method of operation of a computer for managing a plurality of pieces of drawing data representing a set of components, includes: determining whether target drawing data included in the plurality of pieces of drawing data is related to existing drawing data that has been already stored; acquiring record information when the record information associated with the existing drawing data is stored in a case in which a result of the determining is affirmative; and transmitting record display information for displaying the record information to a terminal. The determining includes determining whether the existing drawing data represents a component having an attribute identical with an attribute of a component represented by the target drawing data. The attribute represents a material, a surface treatment, a processing method, or a combination of two or more of them.

The present disclosure discloses methods and systems for detecting one or more security patterns in a document submitted at a multi-function device. According to the disclosure the document is received at the multi-function device, wherein the document includes at least textual content. Thereafter, a pre-defined operation is performed to detect edges of the at least textual content. Once detected, one or more text edges are filtered based on one or more templates. Further, based on the one or more templates, shape and color of the filtered one or more text edges are analyzed, and accordingly, the presence of the one or more security patterns is ascertained in the submitted document at the multi-function device.

A screenshot processing method, relating to the technical field of image processing. The method includes: performing contour recognition on the current picture so as to determine, according to the obtained contour recognition result, a candidate capturing area (S210); in response to an instruction of a screenshot operation, determining at least one target capturing area in the current picture according to the instruction of the screenshot operation and the candidate capturing area (S220); and performing the screenshot operation on the current picture on the basis of the at least one target capturing area so as to generate a target image (S230).

Systems and methods for detecting and aligning land property boundaries on aerial imagery are provided. The system receives an aerial imagery having land properties. The system applies a feature encoder having a plurality of levels to the aerial imagery. A first level of the plurality of levels includes a convolution block and a discrete wavelet transform layer. The discrete wavelet transform layer decomposes an input feature tensor to the first level into a low-frequency band and a high-frequency band. The high-frequency band is cached and processed with side-convolutional blocks before the high-frequency band are passed to a feature decoder. The system applies the feature decoder to an output of the feature encoder based at least in part on one of inverse discrete wavelet transform layers. The system determines boundaries of the one or more land properties based at least in part on a boundary cross-entropy loss function.

A method of processing an image is provided. The method includes identifying edge components of a background image by a unit of a predetermined size, identifying input coordinate points of a touch input which is input on a touch screen, determining distorted coordinate points by applying the edge components to the input coordinate points, and displaying a drawing image formed by connecting the distorted coordinate points, on the background image.

Apparatus and methods are provided for extracting and displaying information and data from a stream of information on a pair of smart glasses. One or more content filters describing the desired information and data may be communicated to a content filtering module. The content filtering module may process the stream of information to search for the desired information and data. When the desired information is found within the stream of information, the content filtering module may highlight the desired information, while blurring all other information, within a display on the pair of smart glasses.

Methods and apparatuses for automatic ingestion of data using a rate-limited application programming interface (API) include a computing device that creates structured query objects, each comprising instructions for retrieving data from a repository using the rate-limited API. The computing device requests data from the repository via the rate-limited API using the structured query objects and a plurality of API access tokens, including a) generating data requests, each comprising a structured query object; b) determining a transmission delay for each API access token based upon a current rate limit imposed by the rate-limited API; c) transmitting each data request to the repository via the rate-limited API using an API access token that has a transmission delay below a threshold value; and d) processing data received from the repository in response to each data request. The computing device repeats steps b) through d) until data responsive to each data request is received.

A method of operation of a computer for managing a plurality of pieces of drawing data representing a set of components, includes: determining whether target drawing data included in the plurality of pieces of drawing data is related to existing drawing data that has been already stored; acquiring record information when the record information associated with the existing drawing data is stored in a case in which a result of the determining is affirmative; and transmitting record display information for displaying the record information to a terminal. The determining includes determining whether the existing drawing data represents a component having an attribute identical with an attribute of a component represented by the target drawing data. The attribute represents a material, a surface treatment, a processing method, or a combination of two or more of them.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for determining predicted digital fonts for textual characters within digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems determine textual characters within a target digital image and determine one or more predicted fonts for the textual characters utilizing a font recognition machine learning model to extract features of the textual characters from the target digital image, the font recognition machine learning model comprising parameters learned from synthetic text data comprising sample textual images generated with a multi-attribute probabilistic model across a distribution of text attributes.