Neural Network for Unicode Visual Character Recognition by IJIRST

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 4 | Issue 4 | September 2017 ISSN (online): 2349-6010

Neural Network for Unicode Visual Character Recognition R. Shanmugam Research Scholar Department of Computer Science Muthayammal College of Arts & Science, India

P. Subramaniam Head of Dept. & Associate Professor Department of Computer Science Muthayammal College of Arts & Science, India

Abstract The central aim of this paper is demonstrating the capability of Artificial Neural Network implementations in recognizing extended sets of visual language symbols. The applications of this technique range from document digitizing and protection to handwritten text recognition in handheld devices. The classic difficulty of being able to correctly recognize even typed visual language symbols is the complex irregularity among pictorial representations of the same character due to variations in fonts, styles and size. This indiscretion undoubtedly widens when one deals with handwritten characters. Hence the conventional programming methods of mapping symbol images into matrices, analyzing pixel and/or vector data and trying to decide which symbol corresponds to which quality would give up little or no realistic results. Clearly the needed method will be one that can detect ‘proximity’ of graphic representations to known symbols and make decisions based on this proximity. To implement such proximity algorithms in the conventional programming one needs to write endless code, one for each type of possible irregularity or deviation from the assumed output either in terms of pixel or vector parameters, clearly not a realistic fare. One such network with supervised education rule is the Multi-Layer Perception (MLP) model. It uses the universal Delta Learning Rule for adjusting its weights and can be trained for a set of input/desired output values in a number of iterations. The very nature of this particular model is that it will force the production to one of nearby values if a variation of input is fed to the network that it is not trained for, thus solving the proximity issue. Both concepts will be discussed in the introduction part of this report. Keywords: Neural Networks, Visual Language, Multi-Layer _______________________________________________________________________________________________________ I.

INTRODUCTION

Artificial Neural Networks Modeling systems and functions using neural network mechanisms is a relatively new and developing science in computer technologies. The particular area derives its basis from the way neurons interact and function in the natural animal brain, especially humans. The animal brain is known to operate in massively parallel manner in recognition, reasoning, reaction and damage recovery. All these seemingly sophisticated undertakings are now understood to be attributed to aggregations of very simple algorithms of pattern storage and retrieval. Neurons in the brain communicate with one another across special electrochemical links known as synapses. At a time one neuron can be linked to as many as 10,000 others although links as high as hundred thousands are observed to exist. The typical human brain at birth is estimated to house one hundred billion plus neurons. Such a combination would yield a synaptic connection of 1015, which gives the brain its power in complex spatio-graphical computation. Unlike the animal brain, the traditional computer works in serial mode, which is to mean instructions are executed only one at a time, assuming a uni-processor machine. The illusion of multitasking and real-time interactivity is simulated by the use of high computation speed and process scheduling. In contrast to the natural brain which communicates internally in electrochemical links, that can achieve a maximum speed in milliseconds range, the microprocessor executes instructions in the lower microseconds range. A modern processor such as the Intel Pentium-4 or AMD Opteron making use of multiple pipes and hyper-threading technologies can perform up to 20 MFloPs (Million Floating Point executions) in a single second. It is the inspiration of this speed advantage of artificial machines, and parallel capability of the natural brain that motivated the effort to combine the two and enable performing complex ‘Artificial Intelligence’ tasks believed to be impossible in the past. Although artificial neural networks are currently implemented in the traditional serially operable computer, they still utilize the parallel power of the brain in a simulated manner. Neural networks have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology and physics. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. This sweeping success can be attributed to a few key factors:  Power: Neural networks are very sophisticated modeling techniques capable of modeling extremely complex functions. In particular, neural networks are nonlinear. For many years linear modeling has been the commonly used technique in most modeling domains since linear models have well-known optimization strategies. Where the linear approximation was not valid

Neural Network for Unicode Visual Character Recognition (IJIRST/ Volume 4 / Issue 4/ 011)

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(which was frequently the case) the models suffered accordingly. Neural networks also keep in check the curse of dimensionality problem that bedevils attempts to model nonlinear functions with large numbers of variables. Ease of use: Neural networks learn by example. The neural network user gathers representative data, and then invokes training algorithms to automatically learn the structure of the data. Although the user does need to have some heuristic knowledge of how to select and prepare data, how to select an appropriate neural network, and how to interpret the results, the level of user knowledge needed to successfully apply neural networks is much lower than would be the case using (for example) some more traditional nonlinear statistical methods. The Multi-Layer Perceptron Neural Network Model

To capture the essence of biological neural systems, an artificial neuron is defined as follows: ď&#x20AC; It receives a number of inputs (either from original data, or from the output of other neurons in the neural network). Each input comes via a connection that has a strength (or weight); these weights correspond to synaptic efficacy in a biological neuron. Each neuron also has a single threshold value. The weighted sum of the inputs is formed, and the threshold subtracted, to compose the activation of the neuron (also known as the post-synaptic potential, or PSP, of the neuron). ď&#x20AC; The activation signal is passed through an activation function (also known as a transfer function) to produce the output of the neuron. If the step activation function is used (i.e., the neuron's output is 0 if the input is less than zero, and 1 if the input is greater than or equal to 0) then the neuron acts just like the biological neuron described earlier (subtracting the threshold from the weighted sum and comparing with zero is equivalent to comparing the weighted sum to the threshold). Actually, the step function is rarely used in artificial neural networks, as will be discussed. Note also that weights can be negative, which implies that the synapse has an inhibitory rather than excitatory effect on the neuron: inhibitory neurons are found in the brain. This describes an individual neuron. The next question is: how should neurons be connected together? If a network is to be of any use, there must be inputs (which carry the values of variables of interest in the outside world) and outputs (which form predictions, or control signals). Inputs and outputs correspond to sensory and motor nerves such as those coming from the eyes and leading to the hands. However, there also can be hidden neurons that play an internal role in the network. The input, hidden and output neurons need to be connected together. A typical feed forward network has neurons arranged in a distinct layered topology. The input layer is not really neural at all: these units simply serve to introduce the values of the input variables. The hidden and output layer neurons are each connected to all of the units in the preceding layer. Again, it is possible to define networks that are partially-connected to only some units in the preceding layer; however, for most applications fully-connected networks are better. Network components are prone to a variety of faults such as packet loss, link cut, or node outage. To prevent the faulty components from hindering network applications, it is important to diagnose (i.e., detect and localize) the components that are the root cause of network faults. However, it is also desirable to repair the faulty components to enable them to return to their operational states. Therefore, we focus on network fault correction, by which we mean not only to diagnose, but also to repair all faulty components within a network. In addition, it has been shown that a network outage can bring significant economic loss. For example, the revenue loss due to a 24-hour outage of a Switzerland based Internet service provider can be more than CHF 30 million. II. PROBLEM DEFINITION Existing System Existing monitoring link delays and faults in a service provider or enterprise IP network. Our two-phased approach attempts to minimize both the monitoring infrastructure costs as well as the additional traffic due to probe messages. In the first phase of our approach, we compute the locations of a minimal set of monitoring stations such that all network links are covered, even in the presence of several link failures. Subsequently, in the second phase, we compute a minimal set of probe messages that are transmitted by the stations to measure link delays and isolate network faults. We show that both the station selection problem as well as the probe assignment problem is NP-hard. We then propose greedy approximation algorithms that achieve a logarithmic approximation factor for the station selection problem and a constant factor for the probe assignment problem. Proposed System We propose several efficient heuristics for inferring the best node to be checked in large-scale networks. By extensive simulation, we show that we can infer the best node in at least 95% of time, and that first checking the candidate nodes rather than the most likely faulty nodes can decrease the checking cost of correcting all faulty nodes. As a result, we want to devise a cost effective network fault correction mechanism that corrects all network faults at minimum cost. To diagnose (but not repair) network faults, recent approaches like use all network nodes to collaboratively achieve this. For instance, in hop-by-hop authentication each hop inspects packets received from its previous hop and reports errors when packets are found to be corrupted. While such a distributed infrastructure can accurately pinpoint network faults, deploying and maintaining numerous monitoring points in a large-scale network introduces heavy computational overhead in collecting network statistics and involves complicated administrative management.

Neural Network for Unicode Visual Character Recognition (IJIRST/ Volume 4 / Issue 4/ 011)

We present the optimality results for an end-to-end inference approach to correct (i.e., diagnose and repair) probabilistic network faults at minimum expected cost. One motivating application of using this end-to-end inference approach is an externally managed overlay network, where we cannot directly access and monitor nodes that are independently operated by different administrative domains, but instead we must infer failures via end to-end measurements. We show that first checking the node that is most likely faulty or has the least checking cost does not necessarily minimize the expected cost of correcting all faulty nodes. III. NETWORK FORMATION

Fig. 1: MLP Network

The MLP Network implemented for the purpose of this paper is composed of 3 layers, one input, one hidden and one output. The input layer constitutes of 150 neurons which receive pixel binary data from a 10x15 symbol pixel matrix. The size of this matrix was decided taking into consideration the average height and width of character image that can be mapped without introducing any significant pixel noise. The hidden layer constitutes of 250 neurons whose number is decided on the basis of optimal results on a trial and error basis. The output layer is composed of 16 neurons corresponding to the 16-bits of Unicode encoding. IV. SYMBOL IMAGE DETECTION The process of image analysis to detect character symbols by examining pixels is the core part of input set preparation in both the training and testing phase. Symbolic extents are recognized out of an input image file based on the color value of individual pixels, which for the limits of this paper is assumed to be either black RGB(255,0,0,0) or white RGB(255,255,255,255). The input images are assumed to be in bitmap form of any resolution which can be mapped to an internal bitmap object in the Microsoft Visual Studio environment. The procedure also assumes the input image is composed of only characters and any other type of bounding object like a border line is not taken into consideration. The procedure for analyzing images to detect characters is listed in the following algorithms: Determining character lines Enumeration of character lines in a character image (â&#x20AC;&#x2DC;pageâ&#x20AC;&#x2122;) is essential in delimiting the bounds within which the detection can precede. Thus detecting the next character in an image does not necessarily involve scanning the whole image all over again. Testing The testing phase of the implementation is simple and straightforward. Since the program is coded into modular parts the same routines that were used to load, analyze and computer network parameters of input vectors in the training phase can be reused in the testing phase as well. V. IMPLEMENTATION Implementation is the stage of the paper when the theoretical design is turned out into a working system. Thus it can be considered to be the most critical stage in achieving a successful new system and in giving the user, confidence that the new system will work and be effective. The implementation stage involves careful planning, investigation of the existing system and itâ&#x20AC;&#x2122;s constraints on implementation, designing of methods to achieve changeover and evaluation of changeover methods.

Neural Network for Unicode Visual Character Recognition (IJIRST/ Volume 4 / Issue 4/ 011)

VI. RESULTS AND DISCUSSION The network has been trained and tested for a number of widely used font type in the Latin alphabet. Since the implementation of the software is open and the program code is scalable, the inclusion of more number of fonts from any typed language alphabet is straight forward. The necessary steps are preparing the sequence of input symbol images in a single image file (*.bmp [bitmap] extension), typing the corresponding characters in a text file (*.cts [character trainer set] extension) and saving the two in the same folder (both must have the same file name except for their extensions). The application will provide a file opener dialog for the user to locate the *.cts text file and will load the corresponding image file by itself. Although the results listed in the subsequent tables are from a training/testing process of symbol images created with a 72pt. font size the use of any other size is also straight forward by preparing the input/desired output set as explained. The application can be operated with symbol images as small as 20pt font size.  Note: Due to the random valued initialization of weight values results listed represent only typical network performance and exact reproduction might not be obtained with other trials. Performance Observation Influence of parameter variation 1) Increasing the number of iterations has generally a positive proportionality relation to the performance of the network. However in certain cases further increasing the number of epochs has an adverse effect of introducing more number of wrong recognitions. This partially can be attributed to the high value of learning rate parameter as the network approaches its optimal limits and further weight updates result in bypassing the optimal state. With further iterations the network will try to ‘swing’ back to the desired state and back again continuously, with a good chance of missing the optimal state at the final epoch. This phenomenon is known as over learning. 2) The size of the input states is also another direct factor influencing the performance. It is natural that the more number of input symbol set the network is required to be trained for the more it is susceptible for error. Usually the complex and large sized input sets require a large topology network with more number of iterations. For the above maximum set number of 90 symbols the optimal topology reached was one hidden layer of 250 neurons. 3) Learning rate parameter variation also affects the network performance for a given limit of iterations. The less the value of this parameter, the lower the value with which the network updates its weights. This intuitively implies that it will be less likely to face the over learning difficulty discussed above since it will be updating its links slowly and in a more refined manner. But unfortunately this would also imply more number of iterations is required to reach its optimal state. Thus a trade of is needed in order to optimize the overall network performance. The optimal value decided upon for the learning parameter is 150. Pictorial representation overlap anomalies One can easily observe from the results listing that the entry for the ‘Latin Arial’ font type has, in general, the lowest performance among its peers. This has been discovered to arise due to an overlap in the pictorial representation of two of its symbols, namely the upper case letter ‘I’ (‘I’ in Times Roman) and the lower case letter ‘l’ (‘l’ in Times Roman). This would definitely present a logically non-separable recognition task to the network as the training set will be instructing it to output one state for a symbolic image and at some other time another state for the same image. This will be disturbing not only the output vectors of the two characters but also nearby states as well as can be seen in the number of wrong characters. The best state the network can reach in such a case is to train itself to output one vector for both inputs, necessitating a wrong state to one of the output. Still this optimal state can be reached only with more number of iterations which for this implementation was 800. At such high number of epochs the other sets tend to jump into over learning states as discussed above. Orthogonal inseparability Some symbol sequences are orthogonally inseparable. This is to mean there cannot be a vertical line that passes between the two symbols without crossing bitmap areas of either. Such images could not be processed for individual symbols within the limits of the paper since it requires complex image processing algorithms. Some cases are presented below: REFERENCES [1] [2] [3] [4] [5] [6]

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Neural Network for Unicode Visual Character Recognition (IJIRST/ Volume 4 / Issue 4/ 011) [45] M.Ravi & A.S.Syed Navaz "Rough Set Based Grid Computing Service in Wireless Network" November - 2016, International Research Journal of Engineering and Technology, Vol No - 3, Issue No - 11, pp.1122– 1126. [46] A.S.Syed Navaz, N.Asha & D.Sumathi “Energy Efficient Consumption for Quality Based Sleep Scheduling in Wireless Sensor Networks” March - 2017, ARPN Journal of Engineering and Applied Sciences, Vol No - 12, Issue No - 5, pp.–1494-1498.