Specific character recognition FPGA implementation method and system, storage medium and application

Abstract

The invention belongs to the technical field of image recognition, and discloses a specific character recognition FPGA implementation method and system, a storage medium and application, which are used for detecting the matching degree of a one-dimensional sequence and a plurality of feature sequences, and are expressed in two dimensions as follows: characters in a specific character set are recognized; based on a Bayesian neural network (BNN) and a random calculation theory: the voting system comprises a voting result statistics module, a multi-input comparison module, a multi-voter voting module, a single-voter voting module, a pixel flow matching specific feature module, a 1 counting module and a random sequence generation module. Aiming at an MNIST data set training result, the methodcomprises the following implementation steps: converting input handwritten numbers, converting each pixel into a random calculation theoretical number with a 128bit bit width, sequentially inputting input streams, and obtaining an identification result after a fixed time period after the input is finished. The method has the advantages of being high in recognition speed, high in accuracy, suitablefor hardware implementation, relatively low in resource consumption and expandable in application range.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and in particular relates to a specific character recognition FPGA implementation method, system, storage medium and application. Background technique [0002] At present, with the development of artificial intelligence technology, the application of image recognition technology has penetrated into our lives, such as license plate recognition and mobile phone photo identification. Using the deep neural network model to detect the content of the picture can accurately identify the content in the picture. Bayesian neural networks combine probabilistic modeling with neural networks and give confidence in prediction results. In a Bayesian neural network, neither weight nor bias is a definite value, but a distribution. Therefore, when implementing Bayesian neural network, it is also necessary to generate variables that obey a specific distribution. This problem can be solved according to t...

AI Technical Summary

Problems solved by technology

The problems to be solved in this architecture are: data input, random number generation and reuse, data processing flow, data output mode

Where possible, nonces should be reused, otherwise they can be quite resource-intensive

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