Artificial Neural Network Implementation in OCR and Pattern Recognition.

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Abstract

Artificial Neural Network is one of the technologies which have developed because of Information Technology development itself. Nowadays, more and more large companies are implementing Artificial Neural Network to support their needs. Artificial neural networks are a method of information processing and computation that takes benefit of today's technology. Mimicking the processes present in biological neurons, Artificial Neural Networks are used to predict and learn from a given set of data information. At data analysis neural networks are more robust than statistical methods because of their capability to handle small variations of parameters and noise. Through this ability, Artificial Neural Network can predict more accurately.

Keywords: Artificial Neural Network

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Abstrak

Jaringan Saraf Tiruan adalah salah satu teknologi yang berkembang karena pengaruh besar dari perkembangan dunia teknologi informasi itu sendiri. Belakangan ini, semakin banyak perusahaan-perusahaan besar yang mengimplementasikan Artificial Neural Network untuk menunjang keperluan mereka. Artificial Neural Network adalah suatu metoda dari pemrosesan informasi dan komputasi yang mengambil keuntungan dari teknologi terkini. Meniru dari proses pada saraf biologis, Artificial Neural Network dipakai untuk memprediksi dan belajar dari kumpulan data yang telah diberikan sebelumnya. Pada tahap penganalisaan data, Artificial Neural Network lebih tangguh daripada metode statistik tradisional karena kemampuannya untuk mengatasi varian yang kecil dari parameter-parameter dan gangguan. Melalui kemampuan ini, Artificial Neural Network dapat memprediksi dengan lebih akurat.

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Table of Figures

Figure 1 Sample Model of Neural Network……….15

Figure 2 An Example of Simple Neuron………..20

Figure 3 Simple Neural Network Diagram……….….21

Figure 4 Mathematical Model of Artificial Neural Network………...…23

Figure 5 Interval Activity Model of A Neuron……….……..…..23

Figure 6 Threshold Function……….……...….24

Figure 7 Piecewise-Linear Function……….……..….24

Figure 8 Sigmoid Function……….……..….24

Figure 9 Supervised Learning Diagram……….…….25

Figure 10 Multilayer Perceptron Method……….………..…….26

Figure 11 Learning Neural Network Diagram……….………..…….26

Figure 12 Demonstration Diagram of OCR using ANN……….………..……….27

Figure 12 Demonstration Diagram of OCR using ANN……….………...29

Figure 13 Input Output Pattern Recognition Example……….……….30

Figure 14 Input Output Example, no. 1……….………..30

Figure 15 Input Output Example, no. 2……….………..31

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Table of Tables

Table 1, Before Firing Rule Applied Table………..27

Table 2, After Firing Rule Applied Table……….28

Table 3, After Firing Rule Applied Table……….30

Table 4, Middle Neuron……….30

Table 5, Bottom Neuron……….30

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Chapter I: Introduction

Artificial Neural Networks are being touted as the sign of the future in

computing. They are certainly self learning mechanisms which don’t require the

traditional skills of a programmer. But unfortunately, there are a lot of misconceptions

among some users who expected that these neuron-inspired processors can do

almost anything. These exaggerations have created disappointment among some

potential users who have tried, and failed to solve their problems with neural

networks. Those applications builders have often come to the conclusion that neural

nets are complicated and confusing. Unfortunately that confusion has come from the

industry itself. Many of the articles flooding the people appeared touting a large

assortment of different neural networks, all with unique claims and specific

examples. Currently only a few of these neuron-based paradigms are actually being

used commercially. There is one particular structure, the feed forward which is also

usually called as bottom-up or top-down, back-propagation network, is by far and

away the most popular. Author will explain about this method in chapter 3 of this

report. Most of the other neural network structures represent models for thinking that

are still being developed in the laboratories. Yet all of these networks are simply

tools and as such the only real demand they make is that they require the network

architect to learn how to use them.

I.1 Problem Occurred

Being given a massive numbers of data requires a lot of effort to analyze and

summarize even for a big company with numerous workers. Companies need a new

technology to predict the trend and make decisions according to massive data silos

given to be analyzed. Before the Artificial Neural Network era, all of these tasks were

given to human or to a conventional programmed application. These methods didn’t

give companies accurate predictions like they wanted it to be. With Artificial Neural

Network, system can learns and try to give what the result is although there might be

imperfections of the result itself.

I.2 Goals

Either humans or other computer techniques use Artificial Neural Network to

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category of information has been given to process, a trained neural network can be

considered as an “expert”.

The expected goals of Artificial Neural Networks are:

 To predict patterns

 To recognize patterns

 To detect trends

 To make a model of any collected given data

I.3 Report Boundaries

This report was made for academic purpose of fulfilling the task that given to

the author as a student at Maranatha Christian University. The author didn’t actually

use or experiencing Artificial Neural Network, nor does or tries actual work related to

Artificial Neural Network application or program. This report is considered as

research paper which is made by doing a research information mainly through

internet and websites as references, and author’s knowledge only.

Although this report was made with author’s best effort, but there are still

some boundaries available to the fact which the author explained:

 This report explains what Artificial Neural Network is.

 Although Artificial Neural Network was implemented within a company, it is not guaranteed that company would be better than it used to be.

 Artificial Neural Network cannot do everything like user wanted it to be.

 Artificial Neural Network is not designed to solve all the problems within a company directly or indirectly.

 Artificial Neural Network must learn by sample first.

 These writing doesn’t include on how technically Artificial Neural Network works or run in the application or program.

 Artificial Neural Network has the tendency to predict, not given an exact answer.

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Chapter IV: Conclusion

In summary, artificial neural networks are one of the promises for the future in

computing. They offer an ability to perform tasks outside the scope of traditional

processors. They can recognize patterns within vast data sets and then generalize

those patterns into recommended courses of action. And another important thing is

neural networks learn, they are not programmed.

Yet, even though they are not traditionally programmed, the designing of

neural networks does require a skill. It requires an "art." This art involves the

understanding of the various network topologies, current hardware, current software

tools, the application to be solved, and a strategy to acquire the necessary data to

train the network. This art further involves the selection of learning rules, transfer

functions, summation functions, and how to connect the neurons within the network.

Then, the art of neural networking requires a lot of hard work as data is fed

into the system, performances are monitored, processes tweaked, connections

added, rules modified, and on and on until the network achieves the desired results.

These desired results are statistical in nature. The network is not always right.

It is for that reason that neural networks are finding themselves in applications where

humans are also unable to always be right. Neural networks can now pick stocks,

cull marketing prospects, approve loans, deny credit cards, tweak control systems,

grade coins, and inspect work.

Yet, the future holds even more promises. Neural networks need faster

hardware. They need to become part of hybrid systems which also utilize fuzzy logic

and expert systems, even though neural networks have a huge potential we will only

get the best of them when they are integrated with computing, AI, fuzzy logic and

related subjects.. It is then that these systems will be able to hear speech, read

handwriting, and formulate actions. They will be able to become the intelligence

behind robots that never wear out nor become distracted. It is then that they will

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Xin Yao