Research of Digital Character Recognition Technology Based on BP Algorithm

Transcription

1 Research of Digital Character Recognition Technology Based on BP Algorithm Xianmin Wei Computer and Communication Engineering School of Weifang University Weifang, China Abstract. This paper describes the digital character recognition process and steps. Using artificial neural networks with momentum term and adaptive learning back-propagation algorithm to train and identify the ideal signal and noise signal containing the of characters. By comparing the results to obtain that using the same network with the ideal signal and noise signal for training the network, the system can be more fault-tolerant. Keywords: Neural network, BP algorithm, a noisy digital character recognition. 1 Introduction Digital recognition technology in the field of image processing is an important research direction, and it is one of the hot areas of computer application. It consists of on-line handwriting recognition and off-line handwriting recognition. In the former system, through recording lifting pen, falling pen, on the spatial location of each pixel of handwritten figures, as well as the time between strokes and other information, in processing that information, the system extracts information characteristics by certain rules, then the recognition module compare and and identify the characteristics of the information with characteristics of library, and finally converted into computer language code. the latter compared with the former without Stroke information, it is more difficult, more widely used, such as bank notes, business reports, financial statements, statistical reports and other forms system is a focus of current research, but also a difficulty. This article describes how to use the neural network back propagation algorithm (BP algorithm) for offline handwritten digit recognition. 2 Simple Process of Hand-Written Numbers with BP Algorithm BP algorithm used in a simple digital identification process as "pre" and "BP character recognition," specifically shown in Figure 1. S. Lin and X. Huang (Eds.): CSEE 2011, Part I, CCIS 214, pp , Springer-Verlag Berlin Heidelberg 2011

2 552 X. Wei Fig. 1. BP Number Recognition The premise work of Digital Identification is to change the visual image into binary image with computer processing, which uses a given threshold metod to change pixels in the image into two colors according to a certain standard. However, fonts of binary images blurred in many cases, or spread appearing messy white or dark dots, causing some difficulties to identify, using gradient sharpening methods to sharpen the image, so that the blurred image becomes clear, and can play a role in removing noise. When identification Only by the characteristics of each digital character to determine, so the binary image after sharpening needs to be split into individual characters, for character refinement. Shelling algorithm commonly used, from the boundary of layer by layer to remove the black spots until you find a collection, this collection coincides with the boundary (thickness of 1 or 2.) In order to extract the characteristics of any character, also normalized the digital characters, that is the size of the character transforms into a uniform size, character position (rotation, translation) corrected. Many people believe that regulation of each character image into 5 9 pixels of a binary image is ideal, because the smaller size of the image, the higher the recognition, the faster network training. In fact, compared to identify the character images, 5 9-pixel map is too small. The normalized, the image information is lost a lot, when the image recognition, the accuracy is not high. Experimental results show that the regulation of a character image into pixels binary image is the real ideal. Processed from the characters is split, the extract can best embody the characteristics of the character feature vectors, on behalf of the BP into the network, the network training. Then extract the sample to be identified in the generation of feature vectors into the trained BP network, the character can be identified. Commonly used method of extracting a feature vector extraction method pixel by pixel, frame feature extraction method, extraction of vertical and other statistics. This experiment uses a pixel-by-pixel extraction method. 3 BP Neural Network for Number Identification 3.1 BP Neural Network Structure and Description BP network is a multilayer feedforward networks with one-way transmission. In addition to input and output nodes in the network, there are one or more layers of hidden nodes, nodes in one layer do not couple. Input signal from the input layer nodes in turn pass through the hidden layer, then spread to the output node. The output of each layer is only under the influence of output layer. The node unit characteristics (transfer function) is usually Sigmnid type, of which, a slope parameter for the Sigmnid, by changing the parameter a, will be different slope Sigmnid function.

3 Research of Digital Character Recognition Technology Based on BP Algorithm 553 The basic idea of BP algorithm is: For an input sample, after weights, thresholds, and activation function operation, get a output, and then compared it with the expectations of the sample, if any deviation, from the output began to backpropagation deviation, to adjust the right value and the threshold, and output of network gradually become the same as hope output. Thus, BP algorithm is based on the steepest descent method, the steepest descent method as the inherent disadvantages: falling into local minimum easily, slow convergence and causing oscillation effect, in adjusting the weights to use the momentum method, which acceled convergence, and to some extent reduce the probability of falling into local minimum, but can not completely overcome these shortcomings. To speed up the convergence, also used the adaptive learning. 3.2 Design and Training of Neural Network The goal is to identify 10 numeric characters from 0 to 9. Each character is divided into small pieces of 5 7 to digitize, respectively represented by a vector. 10 input vectors which containing 35 elements is defined as an input vector matrix, vector represents a letter, the corresponding location of a data value is 1, while the other position is 0. There are two types of data such as input: one is in an ideal state of the signal; the other is to use randomly gened noisy signal. The network for fast training, learning, the initial value selected in 0,01-0, 7 between. Connection weights obtained random between -1 and 1, the initial value of the expected distortion is a random between 0 and1. Network through outputing a 10-element output vectors to distinguish these numeric characters, such as character 1 corresponding to the vector, the elements of its first position is 1, while the subsequent location of the element value is 0. After input and output to be determined the network structure can be designed. Layer 1 is the input layer, based on the above analysis of the data to be identified to determine the neural network input layer has 35 nodes; layer 2 is hidden layer, the conventional method for determining the contacts is twice the input layer, but to rely on experience and methods to try to determine the of nodes, through the system error test with different structures to determine the hidden layer nodes is 10 nodes, shown in Table 1. Table 1. Signals Training and Test Error with Noise of Hidden Layer Number of Training error Test error hidden neurons Layer 3 is the output layer, the target output vector which containing 10 data shows that the layer has 10 nodes. Hidden layer and output layer activation function are Sigmnid, S-type function on the network structure shown in Figure 2.

4 554 X. Wei Fig. 2. Logarithmic S-function network structure To find a suitable training methods, and found with the increasing of, training results of sepa BP method or adaptive learning BP are not ideal, but both adaptive learning and momentum term of the BP training algorithm works well, Therefore using this function to train the neural network. In order to produce a certain input vector network fault tolerance, the best way is to use both with an ideal signal and noise signal to train the network. In this study, the first using 15 group ideal signal to train the network; the 2nd using 15 signals with noise first and then the ideal signal with 15 groups on the same network training. With 10 kinds of increasing noise signal, which is obtained by the ideal signal alphabet to add the average of 0, standard deviation from 0.05 to 0.5. Changes in network training error is shown in Figure 3. Fig. 3. Error changes of the training process without noise Observed indicators of these curves shows that training time can be quickly achieved. Meanwhile, in using different levels of noise signal case, respectively, from 0 to 9 s for the tests, the network identification error curve with the noise signal shown in Figure 4. Fig. 4. Recognition error curve

5 Research of Digital Character Recognition Technology Based on BP Algorithm 555 Dotted line in Figure 4 is the error recognition curve without the error training network, solid line is the error recognition curve with the network trained by the error. It can be seen that from Figure 4 the network training error tolerance is greatly improved. 4 Experimental Results and Analysis Identification method of BP neural network, to take the whole character directly as input of neural network. 500 selected characters, where 200 of them are training, the remaining are test data. Test results in Table 2. The results show that: character recognition based on neural network method has strong fault tolerance and a strong adaptive learning ability, it is a good recognition. Table 2. Experimental Results Item Training Test Total Identified Error recognition Rejection Recognition Error recognition % 0% 0% % 3% 3% Rejection Acknowledgments. This paper is funded by 2011 Natural Science Foundation of Shandong Province, its project is 2011ZRA References 1. Bian, Z.: Pattern recognition. Tsinghua University Press, Beijing (2002) 2. Yang, S.: Image pattern recognition technology-vc + +. Tsinghua University Press, Beijing (2005) 3. Chen, Y.: Feedforward network pattern recognition preprocessing method to handwritten digit recognition application. Chinese Academy of Sciences Semiconductors, Beijing (1995) 4. Yang, Y.: Based on Neural Network Handwritten Digit Recognition. East China Geological University 26(4), (2003) 5. Roth, M.W.: SurveyofNeural Network Technologyfor Automatic Target Recognition. IEEE Trans. Neural Networks 1(1), (1993)