Abstract: An image object classification method and system are disclosed. The method is executed by a processor coupled to a memory. The method includes: providing an image file including at least one image object, performing a process of extracting multiple binary-classified characteristics on the image object to obtain a plurality of first results independent of each other in categories, combining the plurality of first results in a manner of dimensionality reduction based on concatenation, performing a process of characteristics abstraction on the combined first results to obtain a second result, and performing a process of characteristics integration on the plurality of first results and the second result in a manner of dot product of matrices to obtain a classification result.

Abstract: An image object labeling method and system are disclosed. The method is executed by a processor coupled to a memory and includes providing an image file; detecting at least one first object on the image file and generating at least one graphic block and attribute thereof; performing a binarization process to present a first graphic feature on a region containing the graphic block in the image file and a second graphic feature on the rest region; combining the results of detecting and the binarization process, and filtering the image file through several gradually reduced masks to show several separated graphic components until number of the separated graphic components and the at least one graphic block are the same; and assigning a label to the separated graphic components according to the attribute, alternatively, outputting a message, receiving a command for adjusting the graphic block and attribute thereof, as the labels.

Abstract: An image object classification method and system are disclosed. The method is executed by a processor coupled to a memory. The method includes: providing an image file including at least one image object, performing a process of extracting multiple binary-classified characteristics on the image object to obtain a plurality of first results independent of each other in categories, combining the plurality of first results in a manner of dimensionality reduction based on concatenation, performing a process of characteristics abstraction on the combined first results to obtain a second result, and performing a process of characteristics integration on the plurality of first results and the second result in a manner of dot product of matrices to obtain a classification result.

Abstract: An image object classification optimizing method and system are disclosed. The method is executed by a processor coupled to a memory. The method includes steps: providing an image file including at least one image object; performing a process of characteristics enhancement on the image object; performing a process of characteristics classification on the enhanced image object by an odd number of two-dimensional masks whose sizes are sequentially doubled, based on a plurality of characteristic parameters of a preferred classification model, to generate a plurality of classification results; and estimating variabilities of the plurality of classification results, sorting the variabilities, and selecting at least one of the classification results whose variability is lower than a variation tolerance as at least one optimization result, according to the sorting result.

Abstract: A method for analyzing a specification parameter of an electronic component includes inputting a package type and at least one engineering drawing image of an electronic component; acquiring a probability value that in each view of the different viewing directions each of the plurality of specification parameter of the electronic component is labeled; taking the view of each of the plurality of specification parameters in the view direction with a highest probability value as a recommended view; performing a box selection on the plurality of specification parameters for at least one engineering drawing image with the same viewing direction as that of the recommended view by an object detection model; and identifying box-selected specification parameters to acquire a size value of identified specification parameters from the at least one engineering drawing image, and converting the size value into a corresponding editable text for output.

Abstract: A character recognition method includes inputting an input image of a document, with the input image including a plurality of characters; selecting the plurality of characters through an object detection module to form at least one character region; separating the plurality of characters in the at least one character region to form a plurality of character boxes; performing calculation to determine a format of a character in each of the plurality of character boxes; recognizing the characters in the at least one character region through an object recognition module to determine a symbol content of the character in each of the plurality of character boxes; and converting the plurality of characters according to the format and symbol content of the character in each of the plurality of character boxes, and outputting corresponding editable characters.

Abstract: A method for analyzing a viewing direction of an electronic component includes inputting a package type and a file image of an electronic component, with the file image having at least one engineering drawing image, and the at least one engineering drawing image being a view of the electronic component in at least one viewing direction; querying and acquiring a viewing direction detection model meeting the package type from a database, with the database storing respective viewing direction detection models of different package types of electronic components; inputting the file image into the viewing direction detection model of the package type to identify the viewing direction of the at least one engineering drawing image; and outputting the viewing direction of the at least one engineering drawing image of the electronic component.

Abstract: An electronic component packaging type classification system using artificial neural network to execute classification; the electronic component packaging system includes a service database, an external database, a feature selection module, a data-integration module and a classification processing module. The service database receives electronic component patterns externally inputted. The external database stores the packaging type data of electronic components. The feature selection module records the packaging type features of the electronic components. The data-integration module performs the data-processing and the normalization for the selected features to obtain the data to be processed. The classification processing module receives the data to be processed and shows the classification result on the service database.

Abstract: An electronic component footprint verification system and a method thereof are provided in the present disclosure. The system is available to an external user for selecting an electronic component footprint to be verified, reading a verification rule checklist in an external database, extracting characteristics of the electronic component footprint, accessing characteristic data from the electronic component footprint, verifying the characteristic data based on the verification rule checklist, and displaying a verification result.

Abstract: An electronic component footprint verification system and a method thereof are provided in the present disclosure. The system is available to an external user for selecting an electronic component footprint to be verified, reading a verification rule checklist in an external database, extracting characteristics of the electronic component footprint, accessing characteristic data from the electronic component footprint, verifying the characteristic data based on the verification rule checklist, and displaying a verification result.