Handwritten character computer identification method

A computer recognition and handwritten character technology, applied in the field of image recognition, can solve the problems of increasing cumulative error and worsening performance, and achieve the effect of improving the convergence speed, improving the time cost, and avoiding the problem of gradient saturation.

Active Publication Date: 2017-11-07
GUIZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When the number of learning layers of CNN increases, their performance in solving the overfitting problem becomes worse
[0007] 3) In the training and evaluation phase, CNN adopts the gradient descent strategy, and adjusts the target gradient through the backpropagation algorithm to mini

Method used

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  • Handwritten character computer identification method

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Embodiment Construction

[0027] Below in conjunction with the accompanying drawings and preferred embodiments, the specific implementation, features and effects of the method for computer recognition of handwritten characters according to the present invention will be described in detail as follows.

[0028] Such as figure 1 Shown, a kind of handwritten character computer recognition method of the present invention, based on the designed secondary convolutional neural network structure, Relu activation function, and the overfitting prevention method based on Dropout and ADAM, form a kind of optimization based on Dropout and ADAM The convolutional neural network algorithm (aconvolution neural network algorithm based on Dropout and ADAM optimizer, MCNN-DA), the main process is as follows:

[0029] Step 1: Pre-train the filter, and initialize the pixel size of the filter to be: P 1 ×P 2 ;

[0030] Step 2: Input the picture data set used for training, process the pictures in the training set into pictu...

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Abstract

The invention discloses a handwritten character computer identification method. The method is characterized in that the method is based on a secondary convolution neural network structure model, the model has 9 layers, the model comprises an input layer, 5 hidden layers, a full connection layer and an output layer, convolutional layers and pooling layer are staggered to form the hidden layers, the model has a Dropout layer behind the full connection layer, and the method comprises the specific steps: pre-training a filter, inputting a picture data set for training, activating a function using a Relu, outputting a characteristic vector and the like. The method is advantageous in that a convergence speed can be accelerated through the method, an over-fitting problem is solved through the method, an accumulation error is reduced through the method, and the recognition rate is improved through the method.

Description

technical field [0001] The invention belongs to the field of image recognition, and in particular relates to a computer recognition method for handwritten characters. Background technique [0002] Convolutional neural network (CNN) has attracted much attention because of its successful application in the fields of object detection, image classification, knowledge acquisition, image semantic segmentation, etc. Improving its performance is a research hotspot. In the prior art, when solving the problem of image target detection, the CPU is used to control the overall process and data scheduling of the convolutional neural network, and the GPU is used to improve the convolution calculation in the neural network unit and the calculation speed of the fully connected layer combined calculation unit, although The learning speed of the neural network has been improved, but it also increases the time cost due to the data conversion and scheduling between the CPU and GPU, and the weak ...

Claims

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Application Information

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IPC IPC(8): G06K9/68G06N3/04G06N3/08
CPCG06N3/08G06V30/2455G06N3/045
Inventor 杨观赐杨静苏志东陈占杰袁庆霓蓝伟文
Owner GUIZHOU UNIV
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