OCR System

Presented By:-

Vijay apurva(9910103462),

From 4th year,CSEGuided By:-

Mr. Ankur

kulhari

The current capacity to translate paper documents

quickly and accurately into machine readable form using

optical character recognition technology augments the

opportunities in document searching and storing, as well

as the automated document processing. A fast response in

translating large collections of image-based electronic

documents into structured electronic documents is still a

problem. The availability of a large number of processing

units in Grid environments and of free optical character

recognition

tools can be exploited to produce a fast translation.

ABSTRACT:-

CONTENTS :-

What is OCR?

When and Why OCR?

Existing System.

Proposed System.

Architecture of OCR.

Algorithms of OCR.

Modules of OCR.

Design of OCR.

Design of Screen shots for OCR.

Conclusion.

WHAT IS OCR? :-

OCR stands for Optical Character

Recognition. It is one such system that allows us to

scan printed, typewritten or hand written text

(numerals, letters or symbols) and/or convert scanned

image in to a computer process able format, either in

the form of a plain text or a word document.

Later the converted documents can be edited, used

or reused in other documents. Thus the documents

become editable.

WHEN AND WHY OCR? :-

OCR is used when recreating a similar document in

paper as a document in electronic form takes more

time.

The converted text files take less space than the

original image file and can be indexed. Hence the use

of OCR adds an advantage to the user who had to

deal with conversion of great amount of paper works

in to electronic form.

EXISTING SYSTEM:-

In the running world there is a growing

demand for the users to convert the printed documents

in to electronic documents for maintaining the security

of their data. Hence the basic OCR system was invented

to convert the data available on papers in to computer

process able documents, So that the documents can be

editable and reusable.

PROPOSED SYSTEM:-

Our proposed system is OCR ON A

GRID INFRASTRUCTURE which is a character recognition

system that supports recognition of the characters of

multiple languages. This feature is what we call grid

infrastructure which eliminates the problem of

heterogeneous character recognition. In this context, Grid

infrastructure means the infrastructure that supports

group of specific set of languages. Thus OCR on a grid

infrastructure is multi-lingual.

ARCHITECTURE :-

The Architecture of the optical character recognition

system on a grid infrastructure consists of the three main

components. They are:-

Scanner

OCR Hardware or Software

Output Interface

Document

Illuminator

Detector

Document Analysis

Character Recognition Contextual

Processing

Scanner

OCR Hard-Ware Or Soft-Ware

Document image

Output Interface

Recognition Results

To application user

TYPES OF TRAINING:-

Basically there are two major types of training using which

we can train a neural network system. They are:-

Supervised Training

Unsupervised Training

FLOWCHART FOR UNSUPERVISED LEARNING:-

KOHONEN NETWORK:-

The Kohonen network is presented with data, but the correct output that corresponds to that data is not specified. Using the Kohonen network this data can be classified into groups.

FLOWCHART FOR KOHONEN TRAINING:-

ALGORITHMS OF OCR:-

TRAINING ALGORITHM:-

One of the most common learning algorithms is called

Hebb’s Rule. This rule was developed to assist with

unsupervised training.

Hebb’s rule is expressed as:

Δ Wi j= µ ai aj (d-a)

MODULES :-

The Modules that were identified in the Optical

Character Recognition system are as follows:-

Document Processing

Neural network System Training

Document Recognition

Document Editing and

Document Searching

DESIGN OF OCR :-

The design of our OCR system can be

best explained with the following diagram:-

Scan

Store

Recognize Editing

Searching

Document and users Database

OVERALL USECASE DIAGRAM:-

end-user1end-user2

Document modification Document deletion

Document recognition

scan documents

store documents

Document processing

<<includes>>

<<includes>>

Document processing

Document editing

administrator

Trains the system

end-user

OVERALL CLASS DIAGRAM:-

Document

docid : integerdocname : Stringdocsize : integerdoctype : String

getDocumentDetails()scanDocument()covertToImage()storeImage()

Editor

cut()copy()paste()new()open()find()

HelpFrame

HEntry

hLineClear()vLineClear()findBounds()

TrainingSet

inputCount : intoutputcount : inttrainingSetCount : int

setInputCount()setOutputCount()setTrainingSetCount()setClassify()

1..*

1

1..*

1

MainScreen

editor()helpFrame()printedFrame()handWrittenFrame()

Entry

recog : intdownSampleLeft : intdownSampleRight : intdownSampleTop : intdownSampleBottom : int

hLineClear()hLineClearWithin()vLineClear()vLineClearWithin()

PrintedFrame

open_action()train_action()topen_action()recogniseAll_action()

1..*

1

1..*

1

KohenNetwork

LearnMethod = 1:intLearnRate = 0.3:doublequitError : double

copyWeights()clearWeights()winner()normalizeInput()

1..*1..* 1..*1..* 1..*1..* 1..*1..*

DESIGN OF SCREEN SHOTS FOR OCR:-

Main Screen

Hand Written Recognition Screen

Scanned Document Recognition Screen

Training Screen

Recognition Screen

Editor Screen

The screenshots that describe the operations carried

out by our system are as follows :-

CONCLUSION:-

The Grid infrastructure used in the implementation

of Optical Character Recognition system can be efficiently

used to speed up the translation of image based

documents into structured documents that are currently

easy to discover, search and process.

The automated entry of data by OCR is one of the most attractive, labor reducing technology

The recognition of new font characters by the system is very easy and quick.

We can edit the information of the documents more conveniently and we can reuse the edited information as and when required.

The extension to software other than editing and searching is topic for future works.

• Training and recognition speeds can be increased greater and greater by making it more user-friendly.

• Many applications exist where it would be desirable to read handwritten entries. Reading handwriting is a very difficult task considering the diversities that exist in ordinary penmanship. However, progress is being made.