Description
Handwritten Digit Classification Using Deep Neural Network In Matlab
ABSTRACT
The recognition system of using image processing has to improve a little bit. Nowadays handwriting recognition system is required to detect the different types of texts and fonts. This will give problems for security reasons. In this paper, we are implementing the handwriting recognition process by using deep neural network algorithms and techniques. A neural network will give the extraordinary performance to classify images, the images which have the content of our requirements. Here we are having two types of images. By combining the database images with input images we can classify the results. We are having database images with different types of writing styles and different types of fonts. Handwritten Digit Classification Using Deep Neural Network
OVERVIEW AND SCOPE
The main objective is to recognize the handwritten using a deep learning technique .it will identify whose handwriting is this. using image processing.
Handwritten Digit Classification Using Deep Neural Network In Matlab
SCOPE OF THE PROJECT:
The main contributions of this project therefore are
- Data Analysis
- Dataset Preprocessing
- Training the Model
- Testing of Dataset
Handwritten Digit Classification Using Deep Neural Network In Matlab
Neural Networks (NN):
Neural Network (NN) and General Regression Neural Networks (GRNN) have similar architectures, but there is a fundamental difference: networks perform classification where the target variable is categorical, whereas general regression neural networks perform regression where the target variable is continuous. If you select a NN/GRNN network, DTREG will automatically select the correct type of network based on the type of target variable. Handwritten Digit Classification Using Deep Neural Network In Matlab
The architecture of a NN:
All NN networks have four layers:?
- Input layer? There is one neuron in the input layer for each predictor variable. In the case of categorical variables, N-1 neurons are used where N is the number of categories. The input neurons (or processing before the input layer) standardize the range of the values by subtracting the median and dividing by the interquartile range. Do the input neurons then feed the values to each of the neurons in the hidden layer.
- Hidden layer? This layer has one neuron for each case in the training data set. The neuron stores the values of the predictor variables for the case along with the target value. When presented with the x vector of input values from the input layer, a hidden neuron computes the Euclidean distance of the test case from the neuron’s center point and then applies the RBF kernel function using the sigma value(s). The resulting value is passed to the neurons in the pattern layer.?
- Pattern layer / Summation layer? The next layer in the network is different for NN networks and for GRNN networks. For NN networks there is one pattern neuron for each category of the target variable. The actual target category of each training case is stored with each hidden neuron; the weighted value coming out of a hidden neuron is fed only to the pattern neuron that corresponds to the hidden neuron’s category. Do the pattern neurons add the values for the class they represent (hence, it is a weighted vote for that category).
For GRNN networks, there are only two neurons in the pattern layer. One neuron is the denominator summation unit the other is the numerator summation unit. The denominator summation unit adds up the weight values coming from each of the hidden neurons. The numerator summation unit adds up the weight values multiplied by the actual target value for each hidden neuron.?
- Decision layer? The decision layer is different for NN and GRNN networks. For NN networks, the decision layer compares the weighted votes for each target category accumulated in the pattern layer and uses the largest vote to predict the target category.?
For GRNN networks, the decision layer divides the value accumulated in the numerator summation unit by the value in the denominator summation unit and uses the result as the predicted target value.
Handwritten Digit Classification Using Deep Neural Network In Matlab
CONCLUSION
In this? we proposed a handwritten recognition using deep learning neural network based on CNN. and also we used image processing to classify images. Dwt and glam feature extract are techniques that we used in this proposed system.
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