license number plate recognition system using android app

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NUMBER PLATE RECOGNITION SYSTEM USING OCR Enrollment No.: 9911102158 Name of Student: Aditya Mishra Name of Supervisor: Mr.Gaurav Saxena June 2015 Submitted in partial fulfillment of the Degree of Bachelor of Technology in Computer Science Engineering DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY, NOIDA

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Page 1: LICENSE NUMBER PLATE RECOGNITION SYSTEM USING ANDROID APP

NUMBER PLATE RECOGNITION SYSTEM USING OCR

Enrollment No.: 9911102158

Name of Student: Aditya Mishra

Name of Supervisor: Mr.Gaurav Saxena

June 2015

Submitted in partial fulfillment of the Degree of

Bachelor of Technology

in

Computer Science Engineering

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING JAYPEE

INSTITUTE OF INFORMATION TECHNOLOGY, NOIDA

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TABLE OF CONTENTS

Chapter No. Topics Page No.

Table of Contents I

Student Declaration II

Certificate from the Supervisor III

Acknowledgement IV

Summary V

List of Figures VI

List of Tables VII

List of Symbols and Acronyms VIII

I Introduction Page No to Page No 1 to 4

General Introduction

Problem Statement

Empirical Study.

Novelty/benefits

Comparison of existing approaches

II Background Study Page No to Page No 5 to 9

Literature Survey

Summary of papers

Integrated summary of the literature studied

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III Analysis, Design and Modeling 10 to 16

Overall Description of the project

Requirements Specifications

Functional and Non Functional requirements

Logical Database Requirement

Design Documentation

Use Case diagrams

Class diagrams / Control Flow Diagrams

Sequence Diagram/Activity diagrams

IV Implementation Details and Issue 17 to 20

Implementation details and issues

Algorithm

Risk Analysis and mitigation plan

V Testing 20 to 23

Testing Plan

Component decomposition and type of testing

required

List all test cases

Limitation

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VI Conclusion 23 to 24

Finding

Conclusion

Future Work

Appendices 25

Refrences 26

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DECLARATION

I hereby declare that this submission is my own work and that, to the best of my knowledge and

belief, it contains no material previously published or written by another person nor material which

has been accepted for the award of any other degree or diploma of the university or other institute of

higher learning, except where due acknowledgment has been made in the text.

Signature:

Place: Name: Aditya Mishra

Date: 1/6/2015 Enrollment No: 9911102158

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CERTIFICATE

This is to certify that the work titled “ THE NUMBER PLATE RECOGNITION SYSTEM

USING OCR ” submitted by “Aditya Mishra” in partial fulfillment for the award of degree of

B. Tech In Computer Science and Engineering of Jaypee Institute of Information Technology

University, Noida, has been carried out under my supervision. This work has not been submitted

partially or wholly to any other University or Institute for the award of this or any other degree or

diploma.

Signature of Supervisor

Name of Supervisor Gaurav Saxena

Designation ASSISTANT PROFESSOR

Date 1/6/2015

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ACKNOWLEDGEMENT

I would like to place on record our deep sense of gratitude to Dr. Shelly Sachdeva, Professor,

Jaypee Institute of Information Technology, Noida, for her generous guidance. I express my sincere

gratitude to my mentor, Mr. Gaurav Saxena, Assistant Professor , Jaypee Institute of Information

Technology, Noida, I am grateful for his assistance, cooperation, stimulating guidance, continuous

encouragement and supervision throughout the course of present work.

Signature of the Student

Name Aditya Mishra

Enrollment Number 9911102158

Date 1/6/2015

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SUMMARY

Automatic Number Plate Recognition (ANPR) is an image processing technology which uses

number (license) plate to identify the vehicle. The objective is to design an efficient automatic

authorized vehicle identification system by using the vehicle number plate. The system is

implemented on the

entrance for security control of a highly restricted area like military zones or area around top

government offices e.g. Parliament, Supreme Court etc. The developed captures the vehicle image.

Vehicle number plate region is extracted using the image segmentation in an image. Optical

character recognition technique is used for the character recognition. The resulting data is then used

to compare with the records on a database so as to come up with the specific information like the

vehicle’s owner, place of registration, address, etc. The system is implemented and simulated in

Android, and it performance is tested on real image. It is observed from the experiment that the

developed system successfully

Signature of Student Signature of Supervisor

Name: Aditya Mishra Name: Gaurav Saxena

Date: 3/1/2015 Date: 3/1/2015

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LIST OF FIGURES

Figure 1 Tabular Comparison ................................................................................................ 4

Figure 2 OCR Block Diagram ............................................................................................... 10

Figure 3 Windows Application Architecture ......................................................................... 11

Figure 4 Mobile Application Architecture ............................................................................. 12

Figure 5 Use Case .................................................................................................................. 15

Figure 6 Class Diagram.......................................................................................................... 16

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LIST OF TABLES

Table 3.1 Risk and mitigation plan...................................................................................... 28

Table 4.2 Test Plan ................................................................................................................ 30

Table 4.3 Testing Environment ............................................................................................. 32

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LIST OF ACRONYMS AND SYMBOLS

ADK – Android Development Kit

SDK – Software Development Kit

GUI – Graphical User Interface

IDE – Integrated Development Environment

HTTP – Hyper Text Transfer Protocol

SQL – Sequential Query Language

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Chapter 1: INTRODUCTION

1.1 General Introduction

Automatic number plate recognition (ANPR) is an image processing technology which uses number

(license) plate to identify the vehicle. The objective is to design an efficient automatic authorized

vehicle identification system by using the vehicle number plate. The system is implemented on the

entrance for security control of a highly restricted area like military zones or area around top

government offices e.g. Parliament, Supreme Court etc. This system can be used by every man for their

security purpose. For general public an android application will installed on their mobile phone. After

that whenever he wants to know the details of any vehicle he just have to capture the image of licence

plate and then that image will be processed and he will get the desired information about that vehicle.

This system is very important and must needed. For defence purposes the developed system first

detects the vehicle and then captures the vehicle image. Vehicle number plate region is extracted using

the image segmentation in an image. Optical character recognition technique (OCR) is used for the

character recognition. The resulting data is then used to compare with the records on a database so as to

come up with the specific information like the identity of owner, place of registration, address, etc. The

system is implemented and simulated in JAVA, and it performance is tested on real image.

Optical Character Recognition :

The goal of Optical Character Recognition (OCR) is to classify optical patterns (often contained in a

digital image) corresponding to alphanumeric or other characters. The process of OCR involves several

steps including segmentation, feature extraction, and classification. Each of these steps is a field unto

itself, and is described briefly here in the context of a Matlab implementation of OCR.

A few examples of OCR applications are listed here. The most common for use OCR is the first item;

people often wish to convert text documents to some sort of digital representation.

1. People wish to scan in a document and have the text of that document available in a word processor.

2. Recognizing license plate numbers

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3. Image to speech conversion

1.2 Problem Statement

To Develop an android application to capture the image of the licence number plate and to obtain all

the information about that vehicle.

1.3 Empirical Study:

Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic translation

of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-

editable text.

OCR is a field of research in pattern recognition, artificial intelligence and machine vision. Though

academic research in the field continues, the focus on OCR has shifted to implementation of proven

techniques. Optical character recognition (using optical techniques such as mirrors and lenses) and

digital character recognition (using scanners and computer algorithms) were originally considered

separate fields. Because very few applications survive that use true optical techniques, the OCR term

has now been broadened to include digital image processing as well.

Early systems required training (the provision of known samples of each character) to read a specific

font. "Intelligent" systems with a high degree of recognition accuracy for most fonts are now common.

Some systems are even capable of reproducing formatted output that closely approximates the original

scanned page including images, columns and other non-textual components.

The United States Postal Service has been using OCR machines to sort mail since 1965 based on

technology devised primarily by the prolific inventor Jacob Rabinow. The first use of OCR in Europe

was by the British General Post Office (GPO). In 1965 it began planning an entire banking system,

the National Giro, using OCR technology, a process that revolutionized bill payment systems in the

UK. Canada Post has been using OCR systems since 1971.

In 1974 Ray Kurzweil started the company Kurzweil Computer Products, Inc. and led development of

the first omni-font optical character recognition system — a computer program capable of recognizing

text printed in any normal font. He decided that the best application of this technology would be to

create a reading machine for the blind, which would allow blind people to have a computer read text to

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them out loud. This device required the invention of two enabling technologies — the CCD flatbed

scanner and the text-to-speech synthesizer.

1992-1996 Commissioned by the U.S. Department of Energy (DOE), Information Science Research

Institute (ISRI) conducted the most authoritative of the Annual Test of OCR Accuracy for 5

consecutive years in the mid-90s. Information Science Research Institute (ISRI) is a research and

development unit of University of Nevada, Las Vegas. ISRI was established in 1990 with funding from

the U.S. Department of Energy. Its mission is to foster the improvement of automated technologies for

understanding machine printed documents.

One study based on recognition of 19th and early 20th century newspaper pages concluded that

character-by-character OCR accuracy for commercial OCR software varied from 71% to 98%; total

accuracy can only be achieved by human review. Other areas—including recognition of hand

printing, cursive handwriting, and printed text in other scripts (especially those East Asian language

characters which have many strokes for a single character)—are still the subject of active research.

1.4 Novelty of Problem

OCR technology has been used from time to time for various purposes. Various kinds of windows

application have been made till now. But using this technology and android together is a unique

concept. This kind of application is not made yet. The unique feature of this application is that it will

contain large amount of data. Anyone who want to know the detail of any vehicle can directly go to this

app and know the information. Even the Police can use this app to capture the criminals. By image they

will instantly know on whose name the vehicle is registered which is used by criminal. So the use of

technology for safety purpose is unique feature.

1.5 Tabular comparison of other existing approaches

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Fig – Tabular comparison of technologies

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Chapter-2: Literature Survey

2.1 Summary of paper studied

Research Papers

Paper 1

Title of paper AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM FOR

VEHICLE IDENTIFICATION USING OPTICAL CHARACTER

RECOGNITION

Authors Muhammad Tahir Qadri, Muhammad Asif

Year of Publication 2009

Publishing details International Conference on Education Technology and Computer (IEEE)

Summary Automatic Number Plate Recognition (ANPR) is an

image processing technology which uses number (license) plate

to identify the vehicle. The objective is to design an efficient

automatic authorized vehicle identification system by using the

vehicle number plate. The developed system first

detects the vehicle and then captures the vehicle image. Vehicle

number plate region is extracted using the image segmentation

in an image. Optical character recognition technique is used

for the character recognition. The resulting data is then used to

compare with the records on a database so as to come up with

the specific information like the vehicle’s owner, place of

registration, address, etc.

Web link http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=

&arnumber=5403292

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Paper 2

Title of paper Optical Character Recognition

Authors Ravina Mithe, Supriya Indalkar, Nilam Divekar

Year of Publication 2013

Publishing details International Journal of Recent Technology and Engineering (IJRTE)

Summary The Optical Character Recognition is a mobile

application. It uses smart mobile phones of android platform. This

paper combines the functionality of Optical Character

Recognition and speech synthesizer. The objective is to develop

user friendly application which performs image to speech

conversion system using android phones. The OCR takes image as

the input, gets text from that image and then converts it into

speech. This system can be useful in various applications like

banking, legal industry, other industries, and home and office

automation. It mainly designed for people who are unable to read

any type of text documents.

.

Web link http://www.ijrte.org/attachments/File/v2i1/A05040

32113.pdf

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Paper 3

Title of paper Automatic license plate recognition using optical character

recognition and template matching on yellow color license plate

Authors Vandini Sharma,Prakash C. Mathpal, Akanksha

Kaushik.

Year of Publication 2014

Publishing details International Journal of Innovative Research in Science,

Engineering and Technology

Summary Automatic license plate recognition is used to recognize the

characters from license plate image. It is

widely used in various areas such as traffic control, robbery, and

surveillance. The proposed method applied on

yellow color license plate. It has two main stages. Firstly, exact

location of the license plate is detected from an input

image by using image acquisition and optical character recognition

and Sobel edge is used for character

segmentation. Secondly, template matching is used to test the

recognized characters with templates.

Web link http://www.ijirset.com/upload/2014/may/111_Auto

matic.pdf

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Paper 4

Title of paper Design of an Optical Character Recognition System for Camera-

based Handheld Devices

Authors

Ayatullah Faruk Mollah, Nabamita Majumder ,Subhadip Basu, and

Mita Nasipuri

Year of Publication 2011

Publishing details International Journal of Computer Science Issues

Summary This paper presents a complete Optical Character Recognition

(OCR) system for camera captured image/graphics embedded textual

documents for handheld devices. At first, text regions are extracted

and skew corrected. Then, these regions are binarized and segmented

into lines and characters. Characters are passed into the recognition

module.. Compared to Tesseract, an open source desktop-based

powerful OCR engine, present recognition accuracy is worth

contributing. Moreover, the developed technique is computationally

efficient and consumes low memory so as to be applicable on

handheld devices.

Web link http://arxiv.org/ftp/arxiv/papers/1109/1109.3317.

pdf

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2.3 Integrated Summary of Literature Studied:

Topic Result

AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM FOR

VEHICLE IDENTIFICATION USING OPTICAL CHARACTER

RECOGNITION

Optical Character Recognition

Automatic license plate recognition using optical character recognition

and template matching on yellow color license plate

Different techniques are

studied and analyzed i.e.

Yellow search Algorithm,

Smearing algorithm.

Configuring camera

devices to computer and to

use them.

How to implement ocr on

android phone.

Table1: Integrated Summary

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Chapter-3: Analysis, Design and Modeling

3.1 Overall description of the project

On Line / Real Time Input

Off Line Input

Fig 2 : OCR Block Diagram

Touch Pad

PC

Scanned Document

Stored

Characters

Grayscale Conversion

Filtering

Thinning

Feature

Extraction

Pattern Recognition

Recognition

Output

Software Domain

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Fig 3- Windows Application Architecture

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Fig 4 - Mobile Application Architecture

Requirements Specifications for windows Application

Hardware Requirements

Hard disk: 10 GB minimum

Ram: 1GB minimum

3 mega pixel camera

Mouse

Keyboard

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Scanner and Monitor

Software Requirements

System Type: 64-bit operating system,x-64 based processor

OS Installed: Windows XP or higher

Matlab installed

Requirements Specifications for mobile Application

Mobile Hardware Requirements

ARM 11 processor or higher

Memory 1 GB

256 MB RAM

Mobile camera 5 Mega pixel

Software Requirements

JAVA – J2ME and J2EE

Operating System-Android Mob OS

3.2 Functional requirements :

1. For static OCR, software should provide a way to load scanned document for recognition

purpose.

2. If scanned image is not having black background and white foreground, facility for image

inversion should be provided by software.

3. Software should process the image and extract characters.

4. User should have facility to save extracted data in format of his interest.

5. For dynamic OCR, the software should recognize characters drawn by user simultaneously.

6. If software is not giving proper output, there should be a way for training the database of

software.

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Other Requirements:

Considerable amount of training data set

The input image is to be in the RGB file format

In case of scanned image, a high quality scanner as well as good paper quality is required. The

resolution of the scanner should be set to a minimum of 300 dots per inch (dpi)..

A first order median filter is used

3.3 Non-Functional Requirements

• Reliability: The system is highly reliable.

• Accessibility: It can be easily accessible i.e click & run.

• Efficiency: Resource consumption for given load is quite low.

• Fault tolerance: Our system is not fault tolerant due to insufficient hardware.

• Robustness: Our system is not capable to cope with errors during execution.

• Scalability: Our project is scalable i.e. we can add more resources to our project without

disturbing the current scenario.

3.4 Logical Database Requirement

SQLite

The system must store all the user information. All the data shall be stored in text-based flat .sqlite

files. For each registered vehicle, name, license number, criminal record shall be stored in one file.

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3.5 Design Diagrams

3.5.1 USE-CASE DIAGRAMS

Our software system can be used to support library environment to create a Digital Library where

several licence plate images are converted into electronic-form for accessing by the users. For this

purpose the printed plates must be recognized before they are converted into electronic-form. The

resulting electronic-documents are accessed by the users like police and general public for reading

and getting information.

Figure 5: Use case Diagram

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3.3.2 Class Diagram

The class diagram gives a clear picture of all the processes involved in the background in

order to carry out the recognition process. It shows all the classes that happens in the

background and as well gives a clear relationship on how they relates with one another to

help recognize the characters in the plates at the end of the day. The class diagram contains

of all the attributes involved in each class or method. It also gives a high clear idea towards

the entire processing of the image, how the image is being processes to cater for recognizing

the characters.

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Figure : Class Diagram

Chapter 4 IMPLEMENTATION DETAILS AND ISSUES

4.1 Implementation

The LPI android application is designed from a user point of view as well as government point of view.

The user friendly design helps the users in accomplishing their task with ease. Attempts have been

made to keep the design simple and understandable.

The screens were designed in XML and the business logic was written in Java. The database used is

SQLite where all the local information related to the users is stored.

The total lines of code written in this application is

Language LOC

Java

1400

XML

42

Table 4.1 Lines of Code

Debugging of the application throughout the development is done using Dalvik Debug Monitor Server

(DDMS). DDMS provides port-forwarding services, screen capture on the device, thread and heap

information on the device, logcat, process, and radio state information.

Graphical User Interface

The user interface is kept simple and understandable. The user need not take any additional effort to

understand the functionality and navigation in the application. The colors are chosen in such a way that

user can easily understand where the input has to be given. Hints are given to help the user in giving

the correct input.

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The following are the main screens and features in this application.

• Home Screen

• Capture Image screen

• Text Screen

• Result Screen

4.1.2 Algorithm

Yellow Search Algorithm :

A yellow search algorithm is used to extract the likelihood ROI in an image. As number plate of is

in yellow background with alphanumeric character written in black, it is easy to detect the plate area by

searching for yellow pixels. The image is search for the yellow colour pixels or some which are closer

to yellow in value. If pixel value is of yellow colour the pixel is set to 1, otherwise the pixel value is set

to 0. The image obtained after the search algorithm is in black and white format.

Edge Detection :

Algorithms for edge detection contain three steps:

• Filtering (removing): Filtering reduces noise.

• Enhancement: Enhancement emphasizes pixels where there is a significant change in local intensity

values and is usually performed by computing the gradient magnitude.

• Detection: Many points in an image have a nonzero value for the gradient, but not all these points can

be considered to be edges. Therefore, some method should be used to determine which points are edge

points.

Frequently, thresholding provides the criterion for detection.

Preprocessing The major cause of failure in detecting the number plate from the vehicle is low quality of image. The

preprocessing algorithm helps in improving the quality of the image or the plate image being inputted

to the system.

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This involves:

• Resize

This involves resizing the image that is been taken for optimization purposes. Resizing is helpful since

image quality differs and in order to localize the number plate or be able to recognize the number plate

on the image resizing will be very helpful.

• Grayscale

4.2 Risk Analysis and Mitigation Plan

Risk

Id

Classification Description of risk Risk area Probability

(P)

Impact(I) Re(P*I)

1 Hardware Incapability of hardware like RAM, Processor, Memory etc

Performance,hardware, Time

High high 8.1

2 Multitenancy (Shared access)

All the users are using the same physical architecture

Security Low Low 0.1

3 Security Critical Data at risk Security High Medium 8.1

4 Security Authentication, authorization, and access control

User, Project Scope, Time

High High 8.1

5 Hardware Processor Performance,

Time

Low High 0.9

6 Ownership User the owner of data Security High Low 0.9

7 Environment Windows Performance,

Time

High Medium 8.1

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8 Personnel

Related

Incompetent Skills Time High High 8.1

9 Personnel

Related

Irregularity Time Medium High 2.7

Table3: Risk Analysis

RATING IMPACT PROBABILITY

HIGH 9 0.9

MEDIUM 3 0.3

LOW 1 0.1

Table4: Impact

Risk Mitigation Plan

Hardware Hardware related issues can be resolved by using powerful processors support, Faster RAMs and Bigger Storage device.

Security Secure connection must be established.

Personnel Related We will try to avoid irregularity.

Environments ARM must be installed on mobile.

Table6: Mitigation Plan

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Chapter 5: Testing

5.1 Testing Plan

Type of Test Will it be

performed?

EXPLANATIONS Software Component

Requirement

Testing

Yes Requirements specification must

contain all the requirements that are

to be solved by our system.

Manual work, need to

plan out all the software

requirements, time needed

to develop, technology to be

used etc.

Unit Yes Testing by which individual units of

source code are tested to determine

Manual check is required

3

0

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if they are fit for use.

Integration Yes Testing wherein individual

components are combined and tested

as a group.

Compiling full part of the

code and testing it together.

Performance Yes Testing to evaluate the input where

the best and most optimal output is

Yielded by the system.

Protocols used ensures

this.

Stress Yes Simulating beyond normal Operational

capacity.

heavy data files .

Compliance No Not needed NA

Volume

Volume

yes

Yes

Large volume of Data

NA

The protocol ensures this.

5.2 Component decomposition and type of testing required

S.No. Various components

that require testing

Type of testing

required

Technique for

writing Test cases

1 API Implemented Unit, Performance,

Volume, Security

White box

2 Database analysis

and results

Unit, Performance,

Stress

Black box

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3 Eclipse tool and

results

Unit, Performance,

Integration

White box

Table4 : Testing Components

5.3 List all test cases

S.No. Input Output Status

1 Activate the LPI software and

execute the app for capturing

image.

App Capture image

without error

Pass

2 Text Extraction Desired outputs

accordingly

Pass

3 Database Analysis Database Matched Pass

Table5 : Test Cases

5.4 Limitations

The application can capture certain portion of image only. Large image should be

cropped.

Camera should be of good quality. Otherwise correct text from image would not be

extracted properly.

There should be proper lighting.

3

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Chapter-6: Conclusion

6.1 Finding

There are various ANPR applications that are already present but they do not contain man

features. Text extraction applications lack the user friendly environment hence in this

application a lot is concentrated on the user friendly environment. There is no android

application available out there. It’s first of its kind.

6.2 Conclusion

In this project, we aimed to develop a plate recognition system using OCR in android and

windows. We got images by capturing images through a camera without noise and tried to

read characters from that image .Android platform is used because of its availability. Every

person nowadays has android cell phone. There any one can use this application through it.So

for user it is very We will also make Mobile application to detect number Plate using OCR.

6.3 Future Work

The application can be improved in many ways and can be extended to support more devices

like the tablets and iOS devices.

The application will also support blurry images or not so good quality images.

More detailed information regarding the number plate will be available.

People can register and put the information on there own

Registering application with Google service and uploading it into Google Play Store.

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(IX)

APPENDIX

Project Plan

Jan '15

• Finding a way to combine interest and mobile computing

Feb'15

Q1

• Learning all about android

• Learning how to use eclipse

Mar'15

•Designing the interface of mobile application

Mar' 15

•Exploring the content for the application

Apr' 15

•Testing the application

May' 15

• Including the option for saving data in SQLite database to access posts when offline

May' 15

• Making it more user friendly

• Including comments functionality

Jun' 15

• Testing and Enhancement

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References

1.http://www.ele.uri.edu/~hansenj/projects/ele585/OCR/OCR.pdf

2.http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5169511&contentType=Co

nfe rence+Publications&queryText%3Dplate+recognition+system

3. http://www.mathworks.com/products/matlab/

4.Gonzalez, R.C. And Woods, R.E.,1992 Digital image Proccessing, AddisonWesley

Publishing Company Inc., UK

5.J.T. Tou and R.C. Gonzalez, Pattern Recognition Principles, Addison-Wesley Publishing

Company, Inc., Reading, Massachusetts, 1974

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ADITYA MISHRA

College Address: Jaypee Institute of Information Technology, A-10,Sector-62 Noida-201307 Uttar Pradesh. Mob: +91-8130118550 Email: [email protected] | [email protected]

Career Objective: To enhance capabilities and skills through a continuous process of learning, following a logical and

humanitarian approach in life, proving efficiency, keeping in view the importance of time for the organization, and through which reaching greater heights in life.

Software Proficiency:

Knowledge of programming languages C, C++,PHP,Mysql

Knowledge of DBMS and SQL. Knowledge and experience in Web Development, Content management systems and languages like HTML, CSS

and basics of Java Scripts ,Android Application development.

Educational Qualification:

JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY Bachelor of Technology, July 2011 – May 2015 (Expected) Currently pursuing B. Tech, VIII semester in the Department of Computer Engineering Cumulative GPA: 5.2/10 which is equivalent to 61%

Montfort Inter College, Mahanagar, Lucknow Intermediate (Class XII) - U.P. STATE BOARD Percentage: 76%

Montfort Inter College, Mahanagar, Lucknow High School (Class X) - U.P. STATE BOARD Percentage: 78%

Work Experience (Internships & Projects):

Summer Intern at MTAIndia, Technology Division, worked on developing Online Dictionary.

Summer Intern at HPES, Technology Division, worked on developing Online Student Portal.

Web development member at JIIT, worked in developing an interface front end for the college fest website’s home page, etc. as per the requirements and backend customization for the website www. Converge13.com

Project Undertaken:

Currently working on developing an android application “Number Plate Recognition using OCR”. In this Project I will develop a mobile application which will capture the picture of any vehicle. After the picture is captured its number plate image will be converted to text and then we will get the details like on whose name the vehicle is registered etc.

Major project on “Stock Market Prediction using Sentiment Analysis” under Mr. Gaurav Saxena, Department of

Computer Science Engineering, Jaypee Institute of Information Technology, Noida. In this project I created a software using PHP language which will take any word(s) as input and will give all the tweets related to that keyword .I hosted this tool on live server awsums.com. After getting all the related tweets I pass them to a classifier which will classify the tweet into positive, negative or neutral. And then by analyzing we can assume whether the market is going down or going up.

Successfully completed a mini project on "Connect Street" an android application for buyer to track moving vendor under Mr.Himanshu Mittal, Professor at Department of CSE, JIIT Noida. In this project I developed an application which will track down nearest hospitals, restaurants ,or moving vendors and will give direction to reach them

Page 39: LICENSE NUMBER PLATE RECOGNITION SYSTEM USING ANDROID APP

Successfully completed a mini project on "Student Portal" a website for college to keep record of their students attendance and student can prepare for higher studies using this portal under Ms.Aksansha Bharadwaj, Professor at Department of CSE, JIIT Noida.

An initiative to develop a dedicated student level network which supports sharing of academic material among students so as to facilitate useful information interchange and minimize academic problems. (http://www.23ads.com) – Developed the whole portal on my own. Fatshops, is a student entrepreneurial initiative which aims to develop a one stop online shopping portal designed especially for people who wants to shop online at low price. (http://www.Fatshops.com.) – Developed the whole website on my own within a very short span of time to quickly open operations for awaiting users.

Achievements:

Organized Inter college Logo Quiz event at Techno-cultural fest Converge of JIIT.

Part of Web Developer team of the cultural fest Converge at JIIT.

Member of the Cricket Team of JIIT. Awarded with a scholarship by U.P Board for meritorious performance in High School examination. Won prizes in various competitions like ,Collage Making, Just-A-Minute, and designing events and thereby Acquiring real-time experience of technical skills, public speaking, crises management and building public relations.

Ability to work in multi-culture and multi-functional team environment as found in my own college JIIT where I

created a culturally and regionally diverse team for awsumz.com

Passionate about using internet & playing table tennis and cricket.

Personal Profile: Name : Aditya Mishra Father : Ashok Kumar Mishra, Legal Advisor, Lucknow University, Lucknow. Mother : Uma Mishra ,Bank Manager, Gramin Bank of Aryavrat,Lucknow. D.O.B. : 12th of Feb, 1994 Nationality : Indian Home Address : 2/315,Vivek Khand ,Gomti Nagar, Lucknow – 226010. (U.P)

(Aditya Mishra)