A computing device includes a processor and a medium storing instructions. The instructions are executable by the processor to: identify, based on a blob detection analysis, a plurality of potential input elements in a graphical user interface (GUI); determine a set of rows including potential input elements that are in a horizontal alignment and in a same size range; determine a set of columns including potential input elements that are in a vertical alignment and in a same size range; determine a set of input elements comprising multiple potential input elements that are located at intersections of the identified set of rows and the identified set of columns; and perform automated testing of the GUI using the determined set of input elements.

A computing device includes a processor and a medium storing instructions. The instructions are executable by the processor to: identify, based on a blob detection analysis, a plurality of potential input elements in a graphical user interface (GUI); determine a set of rows including potential input elements that are in a horizontal alignment and in a same size range; determine a set of columns including potential input elements that are in a vertical alignment and in a same size range; determine a set of input elements comprising multiple potential input elements that are located at intersections of the identified set of rows and the identified set of columns; and perform automated testing of the GUI using the determined set of input elements.

A computer-implemented method includes: acquiring training data, the training data includes training images for a preset vertical type, and the training images include a first training image containing real data of the preset vertical type and a second training image containing virtual data of the preset vertical type ; building a basic model, the basic model includes a deep learning network, and the deep learning network is configured to recognize the training images to extract text data in the training image; and training the basic model by using the training data to obtain the image recognition model.

A computer-implemented method includes: acquiring training data, the training data includes training images for a preset vertical type, and the training images include a first training image containing real data of the preset vertical type and a second training image containing virtual data of the preset vertical type ; building a basic model, the basic model includes a deep learning network, and the deep learning network is configured to recognize the training images to extract text data in the training image; and training the basic model by using the training data to obtain the image recognition model.

A system and method determines from data collected by one or more data collection elements of a computing device a plurality of discrete product storage locations within a product storage system. Each of the plurality of plurality of discrete product storage locations is caused to be associated with at least one cell of a grid comprised of a plurality of cells. A graphical user interface is displayed in a display of the computing device, the graphical user interface including the grid overlayed upon an image of the product storage system. A user may then interact with the graphical user interface to select a one or more of the plurality of cells of the grid and cause product related information captured via use of the one more data collection elements of the computing device to be linked in a memory storage associated with the computing device to the one of the plurality of discrete product storage locations that was associated with the selected at least one cell of the grid.

A system and method determines from data collected by one or more data collection elements of a computing device a plurality of discrete product storage locations within a product storage system. Each of the plurality of plurality of discrete product storage locations is caused to be associated with at least one cell of a grid comprised of a plurality of cells. A graphical user interface is displayed in a display of the computing device, the graphical user interface including the grid overlayed upon an image of the product storage system. A user may then interact with the graphical user interface to select a one or more of the plurality of cells of the grid and cause product related information captured via use of the one more data collection elements of the computing device to be linked in a memory storage associated with the computing device to the one of the plurality of discrete product storage locations that was associated with the selected at least one cell of the grid.

A paper sheet processing method that is to be employed by a paper sheet processing device. The paper sheet processing method involves acquiring image information for a paper sheet, transmitting the acquired image information to an external character recognition device, receiving the results of character recognition by the character recognition device from the character recognition device, and using the results of the character recognition by the character recognition device to process the paper sheet.

A paper sheet processing method that is to be employed by a paper sheet processing device. The paper sheet processing method involves acquiring image information for a paper sheet, transmitting the acquired image information to an external character recognition device, receiving the results of character recognition by the character recognition device from the character recognition device, and using the results of the character recognition by the character recognition device to process the paper sheet.

An example computing platform is configured to: obtain a two-dimensional drawing of a portion of a construction project; perform an image processing analysis of the two-dimensional drawing to identify one or more location entities within the two-dimensional drawing; derive embeddings for each location entity in the two-dimensional drawing; based on the derived embeddings, determine relationships between the one or more location entities; and based on the determined relationships between the one or more location entities, generate a location entity data taxonomy that includes each identified location entity as a respective node that is related to at least one other location entity.

An example computing platform is configured to: obtain a two-dimensional drawing of a portion of a construction project; perform an image processing analysis of the two-dimensional drawing to identify one or more location entities within the two-dimensional drawing; derive embeddings for each location entity in the two-dimensional drawing; based on the derived embeddings, determine relationships between the one or more location entities; and based on the determined relationships between the one or more location entities, generate a location entity data taxonomy that includes each identified location entity as a respective node that is related to at least one other location entity.