Abstract: A license plate identification method is provided, including the following steps of: obtaining a to-be-processed image; obtaining a plurality of feature maps including target features through a feature map extraction module; obtaining at least one region including the target feature in each feature map and giving each frame of each feature map scores corresponding to the target features through a target location extraction module; classifying each frame in each feature map according to the scores through a target candidate classification module and retaining at least one region that corresponds to character features; and obtaining a license plate identification result according to the region that corresponds to the character feature through a voting/statistics module.

Abstract: A license plate identification method is provided, including steps of: obtaining a to-be-processed image including all characters on a license plate; extracting several feature maps corresponding to character features of the to-be-processed image through a feature map extraction module; for each of the characters, extracting a block and a coordinate according to the feature maps through a character identification model based on a neural network; and obtaining a license plate identification result according to the respective blocks and the respective coordinates of the characters.

Abstract: A neural network system includes at least one memory and at least one processor. The memory is configured to store a front-end neural network, an encoding neural network, a decoding neural network and a back-end neural network. The processor is configured to execute the front-end neural network, the encoding neural network, the decoding neural network and the back-end neural network in the memory to perform operations including: utilizing the front-end neural network to output feature data; utilizing the encoding neural network to compress the feature data, and output compressed data which correspond to the feature data; utilizing the decoding neural network to decompress the compressed data, and output decompressed data which correspond to the feature data; and utilizing the back-end neural network to perform corresponding operations based on the decompressed data. A method of operating a neural network system is also disclosed herein.

Abstract: A license plate identification method is provided, including steps of: obtaining a to-be-processed image including all characters on a license plate; extracting several feature maps corresponding to character features of the to-be-processed image through a feature map extraction module; for each of the characters, extracting a block and a coordinate according to the feature maps through a character identification model based on a neural network; and obtaining a license plate identification result according to the respective blocks and the respective coordinates of the characters.

Abstract: An object-recognition method using simulated object images is provided. The method includes the steps of: (A) obtaining an object-image set including a plurality of object images and a background-image set including a plurality of background images; (B) generating a simulated-object-image set including a plurality of simulated object images according to the object-image set and the background-image set; (C) training an object-recognition model according to the simulated-object-image set; and (D) inputting a to-be-tested image obtained from a to-be-tested scene to the object-recognition model to obtain an object-recognition result.