Objective

2

The recognition of Chinese characters has a broad application prospect in Chinese automatic processing and intelligent input. It is an important subject in the field of pattern recognition. With the emergence of the new technology of deep learning in recent years

the recognition of Chinese characters based on a deep convolutional neural network has made a breakthrough in theoretical method and actual performance. However

many problems still exist

such as the need for a large sample size

long training time

and great difficulty in parameter tuning. Thus

achieving the best identification result for Chinese characters

which belong to numerous categories

is difficult.

Method

2

An end-to-end deep convolutional neural network model was proposed for processing unscreened images with printed and handwritten Chinese characters. Regardless of the additional layers

such as batch normalization and dropout layers

the network mainly consisted of three convolutional layers

two pooling layers

one fully connected layer

and a softmax regression layer. This paper proposed the data augmentation method

which comprehensively adopted a wave distortion

translation

rotation

and zooming

to solve the problem of a small sample size. The translation and zooming scale

the rotation angles

and a large number of pseudo-samples were randomly generated by controlling the amplitude and period of the sine function that caused the wave distortion. The overall structure of the characters could not be changed

and the number of the trainset samples could be increased to infinity. Advanced strategies

such as batch normalization and fine-tuning the model by combining two optimizers

namely

stochastic gradient descent (SGD) and adaptive moment estimation (Adam)

were used to reduce the difficulty of parameter adjustment and the long model training duration. Batch normalization refers to normalizing the input data for each training mini-batch in the process of stochastic gradient descent. Thus

the probability distribution in each dimension becomes a stable probability distribution with mean 0 and standard deviation 1. We define

internal covariate shift

as the change in the distribution of network activations due to the change in network parameters during training. The network should learn to adapt to different distributions at each iteration

which will greatly reduce the training speed of the network. Batch normalization is an effective way to solve this problem. In the proposed network

the batch normalization layer was placed in front of the activation function layer. In the classic convolutional neural network

the mini-batch stochastic gradient descent method is usually adopted during the training process. However

selecting suitable hyper-parameters is difficult. Parameter selection

such as learning rate and initial weight

greatly affects training speed and classification results. Adam is an algorithm for first-order gradient-based optimization of stochastic objective functions based on adaptive estimates of lower-order moments. The method computes individual adaptive learning rates for different parameters from estimates of the first and second moments of the gradients. The greatest advantage of the method is that the magnitudes of parameter updates are invariant to the rescaling of the gradient and that the training speed can be accelerated tremendously. However

the single use of this method cannot ensure state-of-the-art results. Therefore

this paper presents a new training method that combines the novel optimization method

Adam

and the traditional method

SGD. We divided the training process into two steps. First

we adopted Adam to adjust the parameter

such as learning rate

to avoid manual adjustment and make the network coverage immediately. This process lasted for 200 iterations

and the best model was saved after the first training step. Second

SGD was used to further fine-tune the trained model with a minimal learning rate to achieve the best classification result. The initial learning rate was set to 0.0001 in this step and exponentially decayed. Through these methods

the network performed well in terms of training speed and generalization ability.

Result

2

A seven-layer deep model was trained to categorize 3

755 Chinese characters

and the recognition accuracy rate reached 98.336%. The contribution of each proposed method to improve the final effect of the model was verified by several sets of comparative experiments.The recognition rate of the model increased by 8.0%

0.3%

and 1.4% by using data augmentation

combining the two kinds of optimizers

and using batch normalization

respectively.The training time of the model was 483 and 43 minutes less than when SGD was used and batch normalization was not used

respectively.

Conclusion

2

The workload of extracting features is manually reduced compared with traditional recognition methods that use handcrafted features in combination with convolutional neural networks in the reference paper. Our proposed method achieves superior performance because it has a higher recognition rate

stronger extraction ability

and shorter training time compared with the classic convolutional neural network.