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Automatic Voting System Counting and classification of candidates votes

Voting is a painful process especially in calculating votes and recognizing the voted candidate

and summing votes to determine the winner of process Due to all this we decided to build a

system to run in parallel with existing voting system to lower the overhead and cost of Voting

Process and increasing accuracy.

2011-2012 Computer Engineering Depart Shoubra Faculty

Project members:

1- Ahmed Rashad Basuony (ahmedrashad.1990@gmail.com)

2- Ahmed Ali Ibrahim (ahmed.saraya90@gmail.com)

3- Mohab Tarek El-Shishtawy (mohab123@gmail.com)

Automatic Voting System

Contents

Objective

Technology used

Steps of Analysis A. Image processing

1. Resizing

2. Converting To gray

3. Enhancing Edges

4. Thresholding

5. Getting Data

B. Neural Network 1. Logestic Regression Algorithm

2. Cost Function

3. Training and updating Theta

4. Testing

C. GUI

Future Work

Objective

Voting is a painful process especially in calculating votes and recognizing the voted candidate

and summing votes to determine the winner of process.

Due to all this we decided to build a system to run in parallel with existing voting system to

lower the overhead and cost of Voting Process and increasing accuracy.

So it will works as follow it takes ordinary voted paper recognizing where is the check sign

detecting that candidate and increase his votes by one if the correct sign detected, system also

capable of calculating overall votes for every candidate and announce the winner, calculating

correct votes and invalid ones.

The system not only automate the process of calculating votes but also has a high accuracy

ratio for detecting different signs that used to vote whether it’s a check sign, line, reverse signs,

or any correct signs and isolate all noise due to faulted papers or poor scanning condition, it

reached in some cases to 0.999 in accuracy in detecting signs.

Technology Used

We build our system entirely on Java platform , we chose it for its simplicity for using and not

depending on specific OS as it can run anywhere also for high OOP power that enable us to

reuse it object or extending our work in future or modifying the system to adapt any condition.

Using java enabled us from implementing image processing modules from scratch choosing

what is best for our condition for detecting scanned photos and also implementing Neural

Network module reducing complexity for detecting and though time required for overall

process.

Steps of Analysis

The system consist of three main components Image processing module responsible for pre-

processing of scanned image eliminating noise and enhancing edges and reducing amount of

data that used by neural though enhancing its accuracy and speed.

The second module is Neural Network module used to train our network to different valid signs

and calculating percentage of each sign according to trained data and knowing the difference

between a valid sign (correct input) and noise or non-valid sign (incorrect input).

Third module consist of GUI that enable the employee from using the system enabling him form

choosing the database of candidate load and show it then choosing batch directory of scanned

images then it will automatically calculate number of votes, correct ones and invalid ones and

announcing the winner candidate.

A) Image Processing

Our system used multiple image processing techniques for reducing noise we will discuss

them separately showing importance of every one.

1- Resizing

We use resizing technique to unify our work on all images whatever input data or images ;

our system will automatically takes the picture applying resizing with a special technique

without losing any important data to reduce processing image time.

2- Converting To Gray

This part takes the image converting it to gray scaled image by calculating the average of

RGB component of picture taken as we don’t need the complexity of colored image in our

processing.

3- Enhancing Edges

This module enables us from applying a special filter on whole image detect its edges

which is very important in our case as it used for determining vertical and horizontal edges

for the scanned paper return the position of every vertical or horizontal line in picture as

we use this to separate the square containing the check sign

4- Thresholding

Again we use another technique and this one called thresholding it’s used to convert the

gray-scaled image to one bit binary of only black and white enhancing important data and

convert it pure white and all other noise or unwanted data converted to black; this again

reduced complexity one more time that used afterwards to train neural network and used

for detecting when system is running.

5- Getting Data

After applying all this operation on the image we want features of the image in this stage

the square that contain the check sign so we need to convert it to data for using in neural

network as we can’t input pure images to Neural module so we used a technique that

collects pixels of images in one-dimensional array carrying it’s features in another words it

contain only 1’s or 0’s representing every black and white pixels of square that contain the

check sign.

B) Neural Network

As simplicity of our detecting white sign on Black Square we chose a simple neural

technique of Single-neuron.

1- Logistic Regression

Logistic regression low complexity suits the limited nature of problem where time is

critical and output is only 0 (wrong sign) and 1 (valid sign)

Hypothesis

It’s a classification technique where

2- Cost Function

Gradient

3- Training and updating theta

Neural network in this project trained on multiple data-sets of different check signs in

different orientations also reversed signs and for negative output we trained our neural on

empty black boxes and black boxes with noise or incorrect signs to ensure its accuracy, the

training-set (batch) is around 100 different (images) with run learning iterations 100 times

and minimum error of 0.04 also we used technique of randomizing input data to increase

accuracy.

4- Testing

After applying previous training and storing (theta) in separated file our testing hit-ratio of

correct signs to probability of 0.999 and for false or noised signs to 0.02 to 0.03.

C) GUI

This module consists of main screen showing different candidates and their properties from

names, numbers in list, type of candidate, and collected votes it capable of reading from

and comma separated files congaing database of candidates.

With a special screen of navigating user can use select scanned images from anywhere in PC

to start scan and calculate votes.

Also GUI announcing the winner of Elections based on max votes taken and calculates

number of total, correct and incorrect votes for whole scanned images.

Future Work

As the system based on java and OOP modules it can be extended easily adding new features or

modifying the system to run in any environment.

So this can be done in future work:-

1- Decrease reading image time and whole process time by modifying algorithm.

2- Increasing ability to identify multiple columns images that contain wide rand of

candidates.

3- Adding more features to GUI or changing winner methods according to each country

system.

4- Classifying candidates and generating charts of votes correct and incorrect.