Neural Networks & Fuzzy Logic. Alphabet Identification: A MATLAB Implementation using a feed-forward neural network.

Transcription

1 Neural Networks & Fuzzy Logic Alphabet Identification: A MATLAB Implementation using a feed-forward neural network Sudharshan Suresh

2 Problem Definition: To identify an input alphabet between A-Z using a feed-forward neural network and the MATLAB Neural Network Toolbox, and ensure highaccuracy in the same. To train a neural network, using established ground-truth data, and random noise signal so as to give least error. Compare two separately trained neural networks and evaluate the percentage error as per noise ratio. Keywords: Neural Networks, Machine Learning, Image Processing, MATLAB Theory: A feedforward neural network is one of the first and simplest artificial neural network designed. The connections between the units and nodes are not cyclic in nature, and information flows in only one direction- forward. Thus its path is traced through the input nodes, hidden nodes and to the output nodes, in a direct fashion.

3 Data enters at the inputs and passes through the network, layer by layer, until it arrives at the outputs. During normal operation, that is when it acts as a classifier, there is no feedback between layers. This is why they are called feedforward neural networks. In order to build an ideal alphabet classifier, one must have the salient attributes of each object in the class. This is identified via a 5x7 matrix definition for each of the 26 alphabets. This data is fed, along with the required output of the network for every condition. Ideal alphabet classes fed by me to the neural network for training Using the function train, we divide the dataset into training, validation and test sets. Here each set has a specific function Training: Updates the network, adjusts weights based on fitting algorithm. Validation: Ensures the network doesn t overfit the data, by introducing a new set of inputs. Testing: An independent metric that decides the suitability of the network to new samples. The aforementioned training ends when there is no longer a scope for improvement on the training/validation sets.

4 MATLAB Code: I. Neural_Implement function: %script that defines, trains and validates the % accuracy of the ff-neural %networks in alphabet identification. % % %define 5x7 logical character layouts for all 26 alphabets. This is the ground truth data we consider the training with. close all; A = [ B = [ ]'; C = [ ]'; D = [ ]'; E = [ ]'; F = [ ]'; G = [ ]'; H = [ I = [ ]'; J = [ ]'; K = [ L = [ ]'; M = [ N = [

5 O = [ ]'; P = [ ]'; Q = [ ]'; R = [ S = [ ]'; T = [ ]'; U = [ ]'; V = [ ]'; W = [ X = [ Y = [ ]'; Z = [ ]'; X = [A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y, Z]; T = eye(26); noise = [ ]; setdemorandstream(pi); %generates random stream for accurate results % % %ideal neural network training and validation ff_neural_net = feedforwardnet(25); %feed forward neural net of given hidden layer size

6 view(ff_neural_net) ff_neural_net.dividefcn = ''; ff_neural_net = train(ff_neural_net,x,t,nnmatlab); %train neural net %creates 20 copies of each alphabet, adding random noise to the input X_noise matrices to test neural net accuracy no_of_copies = 20; X_noise = min(max(repmat(x,1,no_of_copies)+randn(35,26*no_of_copies)*0.2,0),1); T_noise = repmat(t,1,no_of_copies); for i = 1:length(noise) Xtest = min(max(repmat(x,1,no_of_copies)+randn(35,26*no_of_copies)*noi se(i),0),1); Out_1 = ff_neural_net(xtest); error_net_1(i) = sum(sum(abs(t_noisecompet(out_1))))/(26*no_of_copies*2); end % % %Additive noise neural network training and validation ff_neural_net_with_noise = feedforwardnet(25); ff_neural_net_with_noise = train(ff_neural_net_with_noise,x_noise,t_noise,nnmatlab); for i = 1:length(noise) Xtest = min(max(repmat(x,1,no_of_copies)+randn(35,26*no_of_copies)*noi se(i),0),1); Out_2 = ff_neural_net_with_noise(xtest); error_net_2(i) = sum(sum(abs(t_noisecompet(out_2))))/(26*no_of_copies*2); end % % % comparison between both networks

7 figure plot(noise,error_net_1*100,'--',noise,error_net_2*100); title('% alphabet misrecognised'); xlabel('noise value'); ylabel('errors'); legend('ff neural net','ff neural net with noise','location','northwest') % % II. Test_Image script: % script to identify input letter image and observe output of trained neural network close all; clc, clear; Alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'; % Neural_Assignment %define, train and validate neural network Input_letter = imread('c:\users\sudharshan\documents\course MATERIAL\6th Sem\NNet and Fuzzy Logic\test_letters\v.jpg'); Input_letter = im2double(input_letter); %convert data type Input_letter = imcomplement(imresize(input_letter,[7 5])); %reshape image to 5x7 type imshow(input_letter); Input_letter = reshape(input_letter,[35 1]); %convert matrix to single column vector Y = ff_neural_net(input_letter); fprintf('the predicted alphabet - %c\n', Alphabet(find(compet(Y)))) %predicted alphabet

8 Implementation: A. Training first neural network Neural Network Structure being considered Consists of an input, hidden and output layer with a hidden layer size of 25, one for each alphabet. Using the neural network toolbox of MATLAB, we train the above to get the ideal parameters. Training neural net using nntraintool

9 B. Observing Outputs for input images: Input Images in their true form The given high-resolution images of letters were used as input, with them first being simplified to 5x7 grids to use as neural network input. Upon running the program, the output layer correctly predicts the class, and simple code helps us represent the output value. Output

10 C. Training network to handle noisy data: The above may work well and good for ideal inputs, however we must account for noise and disturbances in the input as well. For this, we introduce the training set as the same 5x7 grids of A-Z, only with additive random noise. Noisy data fed by me to the neural network for training Undergoing training for the new dataset

11 D. Comparison: Now that we have trained both networks, we must analyse its performance to a given noisy dataset. The ground truth data is taken, with noise added in varied proportions, and the accuracy of both networks in correctly predicting the output vs. the noise amount is plotted. It can be seen that the neural network trained with noisy data is far more efficient, showing negligible maximum error, as compared to 45% by the previous attempt. Thus, we can increase accuracy of alphabet detection.

12 Conclusion and Result: Character recognition, having been successfully implemented, is an ideal starting point to implement complex Optical Character Recognition (OCR) problems. Apart from using the modified ground truth as input, images of Times New Roman letters were also successfully classified. The addition of noise serves to increase the accuracy of the network, as it increases it robustness to minor deviations.

13 Bibliography: nalpaper.pdf