INTRODUCTION

Now-a-days the increase in popularity of portable computing devices such as

PDAs and handheld computers, non keyboard based methods for data entry are

receiving more attention in the research communities and commercial sector. The

most promising options are pen-based and voice-based inputs. Digitizing devices

like Smart Boards and computing platforms such as the IBM ThinkPad TransNote

and Tablet PCs, have a pen based user interface. Such devices, which generate

handwritten documents with online or dynamic (temporal) information, require

efficient algorithms for processing and retrieving handwritten data. Online

documents may be written in different languages and scripts. Most of the text

recognition algorithms are designed to work with a particular script and treat any

input text has being written only in the script under consideration. Therefore, an

online document analyzer must first identify the script before employing a

particular algorithm for text recognition. Fig.1.1 shows an example of a document

page containing six different scripts.

Note that a typical multilingual, online document contains two or three scripts. The

document page shown in figure 1 was created by us for illustrating the results.

Note that the writing style in English is mixed, with both cursive and handprint

words. For lexicon-based recognizers, it may be helpful to identify the specific

language of the text if the same script is used by multiple languages.

A manuscript is defined as a graphical form of a writing system. A specific script

like Roman may be used by multiple languages such as English, German and

French. The six scripts considered in this work, named Arabic, Cyrillic, Devnagari,

Han, Hebrew, and Roman, cover the languages used by a majority of the world

population (see Fig. 1). The general class of Han-based scripts includes Chinese,

Japanese, and Korean (we do not consider Kana or Han-Gul). Devnagari script is

used by many Indian languages, including Hindi, Sanskrit, Marathi, and

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Rajasthani. Arabic script is used by Arabic, Farsi, Urdu, etc. Roman script is used

by many European languages like English, German, French, and Italian. We

attempt to solve the problem of script recognition, where either an entire document

or a part of a document (up to word level) is classified into one of the six scripts

mentioned above, with the aim of facilitating text recognition or retrieval. The

problem of identifying the actual language often involves recognizing the text and

identifying specific words or sequence of characters, which is beyond the scope of

this project.

Fig1.1: Online multilingual document containing Cyrillic, Hebrew, Roman, Arabic, Devnagari, and Han scripts.

Most of the published work on automatic script recognition deals with offline

documents, i.e., documents which are either handwritten or printed on a paper and

then scanned to obtain a two-dimensional digital representation.

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LITERATURE REVIEW

Existing System

Most of the published work on automatic script recognition deals with offline

documents, i.e., documents which are either handwritten or printed on a paper and

then scanned to obtain a two-dimensional digital representation. It does not cope

up with the developing technologies such as Smart Boards, IBM ThinkPad

TransNote and Tablet PCs have a pen-based user interface. The Disadvantages

of the existing system are as follows:

The actual languages are identified using projection profiles of words

and character shapes.

The existing system is looking for the presence or absence of specific

shapes in different scripts.

Existing method deals with only spatial characteristics.

Error rate is higher.

Proposed System

The proposed method uses the features of connected components to classify six

different scripts (Arabic, Cyrillic, Devnagari, Han, Hebrew, Roman) and reported a

classification accuracy of 88 percent on document pages. There are few important

aspects of online documents that enable us to process them in a fundamentally

different way than offline documents. The most important characteristics of online

documents are that they capture the direction of strokes while writing the

document. This allows us to analyze the individual strokes and use the additional

direction for both script identification as well as text recognition. In the case of

online documents, segmentation of foreground from the background is a relatively

simple task as the captured data, i.e., the (x; y) coordinates of the locus of the

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stylus, define the characters and any other point on the page belongs to the

background. We use stroke properties as well as the spatial and temporal

information of a collection of strokes to identify the script used in the document.

Unfortunately, the temporal information also introduces additional variability to the

handwritten characters, which creates large intra class variations of strokes in

each of the script classes. The advantages of the proposed system are as follows

This system especially deals with online documents.

The proposed system deals with both spatial and temporal

characteristics of the document.

Easy segmentation of foreground and background of the online

document.

Error rate is lesser.

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SOFTWARE REQUIREMENT SPECIFICATION

Introduction

An Online Calligraphic Manuscript Recognition (OCMR) is part of the so-called Manuscript Recognition Systems, which are applications supporting the direct contact with the user.

Scope of the Project

We describe what features are in the scope of the software and what are not in the

scope of the software to be developed.

In scope

1) Allows the users to write a new script and then save it.

2) Users can write a script and then system can recognise it.

3) Users can view all the scripts saved in the database.

4) Users can delete any/all the saved scripts.

Out of scope

1) The user can recognise only the scripts which are saved in the database.

2) It can generate a error rate to some extent.

Development Methodologies

The following software and languages may be used

a) Java b) Java Plug-Inc) Java Web Startd) Java 2D Toolkitse) Java Swing

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Model Used

Here we will be using the Incremental model; one of the most effective type of model which is most widely used. The incremental model is an intuitive approach to the waterfall model. Multiple development cycles take place here, making the life cycle a “multi-waterfall” cycle. Cycles are divided up into smaller, more easily managed iterations. Each iteration passes through the requirements, design, implementation and testing phases.

Fig 3.1: Incremental Model

Functional Requirements

1) Functional Summary:

The application will ensure that user will be able to write, save, view, recognise and delete scripts without going through any trouble. The scripts saved by a person are reflected in the database in real-time.

2) Performance:

The application on the client side will work as an effective GUI. A system will be needed to store the program files and the database and other files which are necessary for the smooth functioning of the software.

3) Design Constraints:

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There are no design constraints at the time as all aforementioned criteria are satisfied with ease.

General Description

1) Product Perspective:

It is an intended to be areal time application. OCMR should be auserfriendly, quick to

learn and reliable application. It shouls run on both UNIX and windows based operating

systems.

2) Functional Summary:

The application will ensure that the system has a consistent database which can be accessed by the user to access the scripts saved in it. The GUI will work in any system. The software will allow the user to write a script,save and recognise it.

3) User Characteristics:

The application is designed for the script recognition in an online and offline environment . The application will be of a user friendly nature and can be used by any computer literate person. The simplified GUI is easy to use and handle.

Database Design

This section provides a brief description of all the tables in the database that are used by the system.

The database stores the script in six languages provided/specified by the user.

1. The six types of script: Arabic Cyrillic Devnagari Han Hebrew Roman

2. Users:

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A user can store a sript in one of the six different languages and then try to recognise it.

SOFTWARE DESCRIPTION

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The project entitled as ONLINE CALLIGRAPHIC MANUSCRIPT RECOGNITION

is a Real Time application which helps to recognize the different scripts in online

using a common platform. The front end is designed and executed with the

JDK1.5.0 handling the core java part with User interface Swing component.

Development Tools

The development tools provide everything you’ll need for compiling, running,

monitoring, debugging, and documenting your applications. As a new developer,

the main tools you’ll be using are the Java compiler (javac), the Java launcher

(java), and the Java documentation (javadoc).

Application programming Interface (API)

The API provides the core functionality of the Java programming language. It

offers a wide array of useful classes ready for use in your own applications. It

spans everything from basic objects, to networking and security.

Deployment Technologies

The JDK provides standard mechanisms such as Java Web Start and Java Plug-

In, for deploying your applications to end users.

User Interface Toolkits

The Swing and Java 2D toolkits make it possible to create sophisticated Graphical

User Interfaces (GUIs).

UML DIAGRAMS

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1. UML Diagram

User

Fig. 5.1: Use Case Diagram

2. DFD Level 0

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Access recognizable patterns

Calligraphic parameters

Update database

User commands Display

And data information

language

types

Sensor Storing

Status values

Fig. 5.2: LEVEL 0 Data Flow Diagram

3. DFD Level 1

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Calligraphic

Software

Control

Panel

Sensors

Control Panel

Display

Languages

Database

display

information

user commands configure data

and data configure request

sensor info

start/stop language type

update info

sensor status

Fig. 5.3: LEVEL 1 Data Flow Diagram

4. DFD Level 2

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Control panel

Configure system

Interact with user

sensors

Monitor sensors

Display messages

Control panel display

languages

database

Inception July '10

Planning August '10September '10

Analysis September '10

Construction October '10 - February '11

Testing March '11

Deployment April '11

Sensor information

data

data

sensor

configure request configure data information

data update info

changes sensor ID,

type

update info sensor ID

new data store changes

sensor

status

Fig. 5.4: LEVEL 2 Data Flow Diagram

TIMELINE CHART

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Control panel

sensors

Control panel display

languages

database

Interact with user

Monitor changes

Monitor sensors

Configure system

Display messages

Read sensors

Generate signal

Format for display

Update changes

Start the process

Choose the language and write a new document

Fig 6.1 Time Line Chart

OVERALL PROCESS FLOW DIAGRAM

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Fig. 7.1 Overall Flow Diagram

CONCLUSION AND FUTURE ENHANCEMENT

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We have presented a script identification algorithm to recognize six major scripts in

an online document. The aim is to facilitate text recognition and to allow script-

based retrieval of online handwritten documents. The classification is done at the

word level, which allows us to detect individual words of a particular script present

within the text of another script. The classification accuracies reported here are

much higher than those reported in the case of script identification of offline

handwritten documents, although the reader should bear in mind that the

complexities of the two problems are different.

One of the main areas of improvement in the above algorithm is to develop a

method for accurately identifying text lines and words in a document. We are

currently working on developing statistical methods for robust segmentation of

online documents.The script classification algorithm can also be extended to do

page segmentation, when different regions of the handwritten text are in different

scripts.The results of our System and the efficiency of it to classify the words is

provided in the appendix .

REFERENCESPAPERS

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1. Anoop M. Namboodiri, and Anil K. Jain, “Online Handwritten Script

Recognition” Ieee Transaction On Pattern Analysis And Machine

Intelligence, Vol. 26, no. 1, January 2004.

2. A.L. Spitz, “Determination of the Script and Language Content of Document

Images,” IEEE Trans. Pattern Analysis and Machine Intelligence,

WEBSITES

1. www.ieee.explore.com

2. www.pencomputing.com

3. www.ancientscripts.com

4. www.informit.com

5. www.sun.com

6. www.java.com

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