Method for identifying offline handwritten Chinese characters based on deep separable convolutional neural network

Abstract

The invention discloses a method for identifying offline handwritten Chinese characters based on a deep separable convolutional neural network. According to the method, firstly, an image is subjectedto image cropping and image negative image preprocessing; then a convolutional neural network based on a depth separable convolution is designed, a random gradient descent method and a reverse propagation algorithm is used to carry out a supervised training, and model parameters are saved when a model is converged; finally, the saved model is used to identify a test image to verify a validity of the model. According to the method for identifying offline handwritten Chinese characters based on the deep separable convolutional neural network, the calculation amount of the model and the storage amount can be reduced, such that the offline handwritten Chinese characters identification model based on the neural network can be operated on the mobile terminal device offline; and the image preprocessing and convolutional neural network design are improved by mainly reducing the calculation complexity and the model capacity of the convolutional neural network, such that the method can be applied to computing equipment with limited computing resources and storage resources.

Description

technical field [0001] The invention relates to the field of pattern recognition and image classification, in particular to an off-line handwritten Chinese character recognition method based on a deep separable convolutional neural network. Background technique [0002] Off-line handwritten Chinese character recognition only has two-dimensional image information, compared with online handwritten Chinese character recognition, it has less stroke sequence information, so it has always been a relatively difficult recognition problem in classification recognition. The traditional method is divided into three parts, one is to preprocess the data; the other is to extract the artificially designed features; the third is to classify and identify the features. In recent years, due to the successful application of convolutional neural networks in computer vision, the recognition accuracy of offline handwritten Chinese character recognition has been greatly improved. The convolutional...

AI Technical Summary

Problems solved by technology

In order to solve the deficiencies of the prior art in terms of model capacity and computational complexity, the present invention aims to design a convolutional neural network model with small model capacity and low computational complexity for offline handwritten Chinese character recognition

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