March 2016 ~ software info and opensource related updates

Thursday, March 17, 2016

IMPLEMENTATION OF OCR USING NEURAL NETWORK IEEE Paper and Paper Presentation

Abstract

The aim of the project is to develop OCR software for Tamil character recognition. OCR is an optical character recognition and is the mechanical or electronic translation of images of typewritten or handwritten (usually captured by a scanner) into machine-editable text. OCR is a field of research in pattern recognition, artificial intelligence and machine vision. Character recognition is used most often to describe the ability of computer to translate printer or human writing into text. In this paper we focus on recognition of English alphabet in a given scanned text document with the help of Neural Networks. Using Mat labNeural Network toolbox, we are going to recognize handwritten characters by projecting them on different sized grids. The first step is image acquisition which acquires the scanned image followed by noise filtering, smoothing and normalization of scanned image, rendering image suitable for segmentation where image is decomposed into sub images. Feature Extraction improves recognition rate and misclassification. We going to use character extraction and edge detection algorithm for training the neural network to classify and recognize the handwritten character. Existing Applications which are similar to our application contain many mismatches and errors that will be rectified in our project which increases the accuracy of the text character recognition.

IMPLEMENTATION OF TAMIL-Optical character recognition USING NEURAL NETWORK

Objectives

The objective of this project is to develop robust OCR's for printed Tamil scripts, which can deliver desired performance for possible conversion of legacy, printed documents into electronically accessible format.
It should be boon to the people who uses the proprietary OCR’s.
It will be good arrival to magazine industry.

Abstract

The aim of the project is to develop OCR software for Tamil character recognition.
OCR is an optical character recognition and translation of images of typewritten or handwritten (usually captured by a scanner) into machine-editable text.
In this project, the focus is on recognition of Tamil alphabet in a given scanned text document with the help of Neural Networks.
Handwritten characters are recognized by projecting them on different sized grids using Java.

Introduction

OCR, is the process of translating images of handwritten, typewritten, or printed text into a format understood by machines for the purpose of editing, indexing/searching, and a reduction in storage size.
Optical Character Recognition that would use an Artificial Neural Network as the backend to solve the classification problem.
The input for the OCR problem is pages of scanned text.

Product Functions

Tamil Optical Character Recognition converts the text image into text document.
OCR that includes a way to edit the text directly.
It enables the users to store the text in as a separate file in the system.

Applications of Tamil-OCR

Data entry for business documents, e.g. check, passport, invoice, bank statement and receipt
Automatic insurance documents key information extraction
Extracting business card information into a contact list
More quickly make textual versions of printed documents, e.g. book scanning for Project Gutenberg
Make electronic images of printed documents searchable, e.g. Google Books
Defeating CAPTCHA anti-bot systems, though these are specifically designed to prevent OCR

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.
The existing system is not efficient for the language Tamil and also have lots of errors in detecting the characters.
The existing system consume much more time to recognize the characters from the image.

Proposed System

The proposed Tamil Optical Character Recognition will perform some serious of operation to perform the Recognition process easier and accurate.
To perform the recognition of character faster.
To obtain complete accuracy in the text recognition.
To develop OCR for Tamil language.

Literature Survey

[1] Kauleshwar Prasad, Devvrat C. Nigam, Ashmika Lakhotiya and Dheeren Umre (2013)“Character Recognition Using Neural Network Toolbox”, International Journal of u- and e- Service, Science and Technology

This paper focus on recognition of alphabet in a given scanned text document with the help of neural Networks.
Here we use character extraction and edge detection algorithm for training the neural network to classify and recognize the characters.

[2]VenuGovindaraju, SrirangarajRangaSetlur (2013) “Guide to OCR for Indic Scripts: Document Recognition and Retrieval”. International Journal of Advanced Research in Computer Science and Software Engineering

It helps in developing a new approach to deal with the problem with indic scripts.

[3]Java Neural Network Framework Neuroph,Link: “http: //sourceforge.net/projects/neuroph/?source=directory “

The above website provides the informations about the neuroph.

Operating Environment
Software Requirements:

Windows/Linux: The Tamil optical character recognition application using neural network will operates on windows (XP/7/8), Linux. All device that supports the version of the windows or Linux operating system will be able to run the software.
NetBeans: NetBeans’s extensive GUI features/toolkits make GUI development easy and flexible. The software is developed using NetBeans.
Open Office: Open office is the leading open-source office suite for word processing.

System Features
Language Auto Detection

Tamil-Optical Character Recognition will detect the language based on Tamil Unicode range.
Tamil characters fall within a specific Unicode range.

Character Mapping

The Tamil-OCR will automatically maps the character by defining the box for each character.
The space will act as a delimiter.

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Font & Style Detection

The OCR will automatically detects the font style and the size of the font.

MODULE
Module 1: Image Acquisition

In Image acquisition, the recognition system acquires a scanned image as an input image.
The image should have a specific format such as JPEG, GIF, etc.

Module 2: Preprocessing

The role of pre-processing is to segment the interesting pattern from the background.
The noise filtering, smoothing and normalization should be done in this step.

Module 3: Segmentation

An image of sequence of characters is decomposed into sub-images of individual character.
This labelling provides information about number of characters in the image.

Module 4: Feature Extraction

The features of the characters that are crucial for classifying them at recognition stage are extracted.
Every character image is divided into equal zones.

Module 5: Classification and Recognition

A feed forward back propagation neural network is used in this work for classifying and recognizing the handwritten characters.
The pixels derived from the resized character in the segmentation stage form the input to the classifier.

Module 6: Post Preprocessing

Post-processing stage is the final stage of the proposed recognition system.
It prints the corresponding recognized characters in the structured text form.

Design and Implementation Constraints

The system is designed to identify the character from image, it is necessary to train with each characters for many times.
The training to improve the quality of recognition will pose a difficult challenge.
Other constrains such a noise filtering and segmentation of each character are also worth considering.
The application is meant to be accurate even when dealing with noisy data so each portion must be designed and implemented with efficiency in mind.
It can recognize only Tamil language.

Assumptions and Dependencies

The training should be given for each characters with various size.
It is necessary to convert the image into binary format.
We convert it into grayscale image, then by using the threshold of the grayscale it is converted to binary.

Usecase Diagram

Sequence Diagram

Activity Diagram

Class Diagram

Tamil OCR-GUI

Final View OCR-GUI

Output OCR-GUI

Result

The result of the project is that the Tamil-OCR is implemented for recognizing the Tamil text in the scanned image and to convert it into an editable text format.
This increase the accuracy of the OCR process for the language Tamil.
This increases the most of the Tamil press people to migrate towards free and open source software.
The GUI for Tamil-OCR will make easy for all the peoples who don’t have knowledge about the OCR to use it in a perfect manner.

Conclusion

Our system will be developed for end users who have basic knowledge on Linux/Windows.
It will perform intended operation under almost all circumstances.
The GUI for Tamil-OCR ill make easy for all the peoples who don’t have knowledge about the use of OCR in a perfect manner.
The process of the unit testing involves independent analysis of the system in parts or in units.
This project is OS independent and hence people can work on their desire Operating System.

References

Kauleshwar Prasad, Devvrat C. Nigam, Ashmika Lakhotiya and Dheeren Umre “Character Recognition Using Neural Network Toolbox”, International Journal of u- and e- Service, Science and Technology Vol. 6, No. 1, February, 2013.
Venu Govindaraju, Srirangaraj (Ranga) Setlur “ Guide to OCR for Indic Scripts : Document Recognition and Retrieval”.
Java Neural Network Framework Neuroph, Link: “http://sourceforge.net/projects/neuroph/?source=directory “

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IMPLEMENTATION OF OCR USING NEURAL NETWORK IEEE Paper Download

IMPLEMENTATION OF OCR USING NEURAL NETWORK Paper Presentation ppt download.

IEEE Paper abstract and PPT Download for seminar and paper presentation. Project paper download

IMPLEMENTATION OF TAMIL-OCR USING NEURAL NETWORK IEEE PAPER DONLOAD

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IMPLEMENTATION OF TAMIL-OCR USING NEURAL NETWORK

Abstract

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Project paper for research and paper presentation.

IEEE Paper for final year project

IEEE Pare On Palm Vein Technology Abstract

Iee paper download for free

IMPLEMENTATION OF TAMIL-OCR USING NEURAL NETWORK fULL PAPE R DOWNLOAD

Integration of Sound Signature in Graphical Password Authentication System iEEE PAPER DOWNLOAD

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Integration of Sound Signature in Graphical Password Authentication System

ABSTRACT

Here a graphical password system with a supportive sound signature to increase the remembrance of the password is discussed. In proposed work a click-based graphical password scheme called Cued Click Points (CCP) is presented. In this system a password consists of sequence of some images in which user can select one click-point per image. In addition user is asked to select a sound signature corresponding to each click point this sound signature will be used to help the user in recalling the click point on an image. System showed very good Performance in terms of speed, accuracy, and ease of use. Users preferred CCP to Pass Points, saying that selecting and remembering only one point per image was easier and sound signature helps considerably in recalling the click points. Keywords: Sound signature, Authentication

1. Introduction

Passwords are used for – (a) Authentication (Establishes that the user is who they say they are). (b) Authorization (The process used to decide if the authenticated person is allowed to access specific

information or functions) and (c) Access Control (Restriction of access-includes authentication & authorization). Mostly user select password that is predictable. This happens with both graphical and text based passwords. Users tend to choose memorable password, unfortunately it means that the passwords tend to follow predictable patterns that are easier for attackers to guess. While the predictability problem can be solved by disallowing user choice and assigning passwords to users, this usually leads to usability issues since users cannot easily remember such random passwords. Number of graphical password systems have been developed, Study shows that text-based passwords suffers with both security and usability problems[1][8]. According to a recent news article, a security team at a company ran a network password cracker and within 30 seconds and they identified about 80% of the passwords [2]. It is well know that the human brain is better at recognizing and recalling images than text[3][7], graphical passwords exploit this human characteristic.

2. PREVIOUS WORK

Considerable work has been done in this area,The best known of these systems are Passfaces [4][7]. Brostoff and Sasse (2000) carried out an empirical study of Passfaces, which illustrates well how a graphical password recognition system typically operates. Blonder-style passwords are based on cued

recall. A user clicks on several previously chosen locations in a single image to log in. As implemented by Passlogix Corporation (Boroditsky, 2002), the user chooses several predefined regions in an image as his or her password. To log in the user has to click on the same regions. The problem with this scheme is that the number of predefined regions is small, perhaps a few dozens in a picture. The password may have to be up to 12 clicks for adequate security, again tedious for the user. Another problem of this system is the need for the predefined regions to be readily identifiable. In effect, this requires artificial, cartoon-like images rather than complex, real-world scenes[5][6]. Cued Click Points (CCP) is a proposed alternative to PassPoints. In CCP, users click one point on each of 5 images rather than on five points on one image. It offers cued-recall and introduces visual cues that instantly alert valid users if they have made a mistake when entering their latest click-point (at which point they can cancel their attempt and retry from the beginning). It also makes attacks based on hotspot analysis more challenging. As shown in Figure 1, each click results in showing a next-image, in effect leading users down a “path” as they click on their sequence of points. A wrong click leads down an incorrect path, with an explicit indication of authentication failure only after the final click. Users can choose their images only to the extent that their click-point dictates the next image. If they dislike the resulting images, they could create a new password involving different click-points to get different images.

3. PROPOSED WORK

In the proposed work we have integrated sound signature to help in recalling the password. No system has been devolved so far which uses sound signature in graphical password authentication. Study says that sound signature or tone can be used to recall facts like images, text etc[6]. In daily life we see various examples of recalling an object by the sound related to that object [6]. Our idea is inspired by this novel human ability.

CLICK HER TO DOWNLOAD COMPLETE IEE PAPER ON "Integration of Sound Signature in Graphical Password Authentication System"

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Project paper for research and paper presentation.

IEEE Paper for final year project

IEEE Pare On Palm Vein Technology Abstract

Iee paper download for free

HAPTIC TECHNOLOGY

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HAPTIC TECHNOLOGY

Abstract :

Engineering as it finds its wide range of application in every field not an exceptioneven the medical field. One of the technologies which aid the surgeons to perform even the most complicated surgeries successfully is Virtual Reality. Even though virtual reality is employed to carry out operations the surgeon's attention is one of the most important parameter. If he commits any mistakes it may lead to a dangerous end. So, one may think of a technology that reduces the burdens of a surgeon by providing an efficient interaction to the surgeon than VR. Now our dream came to reality by means of a technology called “HAPTIC TECHNOLOGY”. Haptic is the “science of applying tactile sensation to human interaction with computers”. In our paper we have discussed the basic concepts behind haptic along with the haptic devices and how these devices are interacted to produce sense of touch and force feedback mechanisms.

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Project paper for research and paper presentation.

IEEE Paper for final year project

IEEE Pare On Palm Vein Technology Abstract

Iee paper download for free

PALM VEIN TECHNOLOGY Abstract

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PALM VEIN TECHNOLOGY

ABSTRACT:

With the increase in technology threat to personal data and national security hadalso increased. The methods that were developed to secure important information from outsideintervention were not up to safe mark .There was a need to introduce a technology that securesour data more efficiently from unlawful intervention .Fujitsu has developed a palm vein pattern authentication technology that usesvascular patterns as personal identification data .Vein recognition technology is secure becausethe authentication data exists inside the body and is therefore very difficult to forge. It is highlyaccurate. This technology can be used in various fields like banking, hospitals, governmentoffices, in passport issuing etc. Business growth will be achieved with these solutions byreducing the size of the palm vein sensor and shortening the authentication time.Hand vein is a biometric modality that seems promising as it is acquired in Near Infrared light (NIR), which implies that skin variations and dirtiness are less sensible than invisible light. Moreover, the haemoglobin which flows in the veins is sensible to NIR light, thisway allowing a good quality of acquisition of the hand veins. It is possible to use either the back of the hand or the hand palm. A recent study using back hand vein data and tested with 5sessions per person and 50 persons showed promising results. The main problem of this databaseis the low resolution of the images (images at resolution 132x124 pixels).The first commercialized products have been produced by Hitachi on the back andFujitsu on the palm. They have been patented but only little information is available on them.These companies claim a very low FRR ( False Rejection Rate) at very low FAR (FalseAcceptance Rate) on a huge database ± close to 0% on 140000 hands. Unfortunately at thismoment, there is no public database allowing verifying these figures.In general, in the various papers present in the literature, after the acquisitionphase, some matching algorithms are used such as the Line segment Hausdorff Distance (LHD)method. The LHD method has good experiment results. But, the structure information of palmvein is not as clear as hand vein, so line-based feature is not a good choice for palm veinrecognition. Matching based on minutiae analysis and Hausdorff distance (MHD) was used for hand vein recognition. Minutiae-like feature could also be extracted from palm vein pattern

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Project paper for research and paper presentation.

IEEE Paper for final year project

IEEE Pare On Palm Vein Technology Abstract

Iee paper download for free

SQL GROUP BY Clause

6:10 PM Programming, SQL Commands No comments

The SQL GROUP BY statement is used together with the SQL aggregate functions to group the retrieved data by one or more columns. The GROUP BY concept is one of the most complicated concepts for people new to the SQL language and the easiest way to understand it, is by example.

Remember we used the SUM keyword to calculate the total sales for all stores? What if we want to calculate the total sales for each store? Well, we need to do two things: First, we need to make sure we select the store name as well as total sales. Second, we need to make sure that all the sales figures are grouped by stores. The corresponding SQL syntax is,

SELECT "column_name1", SUM("column_name2")

FROM "table_name"

GROUP BY "column_name1";

Let's illustrate using the following table,

Table Store_Information

Store_Name	Sales	Txn_Date
Los Angeles	1500	Jan-05-1999
San Diego	250	Jan-07-1999
Los Angeles	300	Jan-08-1999
Boston	700	Jan-08-1999

We want to find total sales for each store. To do so, we would key in,

SELECT Store_Name, SUM(Sales)

FROM Store_Information

GROUP BY Store_Name;

Result:

Store_Name	SUM(Sales)
Los Angeles	1800
San Diego	250
Boston	700

SQL Aggregate Functions

5:34 PM Programming, SQL Commands No comments

SQL Aggregate Functions

SQL Aggregate functions return a single value, using values in a table column. Useful aggregate functions:

AVG( ) - Returns the average value
COUNT( ) - Returns the number of rows
FIRST( ) - Returns the first value
LAST( ) - Returns the last value
MAX( ) - Returns the largest value
MIN( ) - Returns the smallest value
SUM( ) - Returns the sum

In this chapter we are going to introduce a new table called Sales, which will have the following columns and data:

OrderID	OrderDate	OrderPrice	OrderQuantity	CustomerName
1	12/22/2005	160	2	Smith
2	08/10/2005	190	2	Johnson
3	07/13/2005	500	5	Baldwin
4	07/15/2005	420	2	Smith
5	12/22/2005	1000	4	Wood
6	10/2/2005	820	4	Smith
7	11/03/2005	2000	2	Baldwin

The SQL COUNT function returns the number of rows in a table satisfying the criteria specified in the WHERE clause. If we want to count how many orders has made a customer with CustomerName of Smith, we will use the following SQL COUNT expression:

SELECT COUNT (*) FROM Sales WHERE CustomerName = 'Smith'

Let's examine the SQL statement above. The COUNT keyword is followed by brackets surrounding the * character. You can replace the * with any of the table's columns, and your statement will return the same result as long as the WHERE condition is the same.

The result of the above SQL statement will be the number 3, because the customer Smith has made 3 orders in total.

If you don't specify a WHERE clause when using COUNT, your statement will simply return the total number of rows in the table, which in our case is 7:

SELECT COUNT(*) FROM Sales

How can we get the number of unique customers that have ordered from our store? We need to use the DISTINCT keyword along with the COUNT function to accomplish that:

SELECT COUNT (DISTINCT CustomerName) FROM Sales

The SQL SUM function is used to select the sum of values from numeric column. Using the Sales table, we can get the sum of all orders with the following SQL SUM statement:

SELECT SUM(OrderPrice) FROM Sales

As with the COUNT function we put the table column that we want to sum, within brackets after the SUM keyword. The result of the above SQL statement is the number 4990.

If we want to know how many items have we sold in total (the sum of OrderQuantity), we need to use this SQL statement:

SELECT SUM(OrderQuantity) FROM Sales

The SQL AVG function retrieves the average value for a numeric column. If we need the average number of items per order, we can retrieve it like this:

SELECT AVG(OrderQuantity) FROM Sales

Of course you can use AVG function with the WHERE clause, thus restricting the data you operate on:

SELECT AVG(OrderQuantity) FROM Sales WHERE OrderPrice > 200

The above SQL expression will return the average OrderQuantity for all orders with OrderPrice greater than 200, which is 17/5.

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The SQL MIN function selects the smallest number from a numeric column. In order to find out what was the minimum price paid for any of the orders in the Sales table, we use the following SQL expression:

SELECT MIN(OrderPrice) FROM Sales

The SQL MAX function retrieves the maximum numeric value from a numeric column. The MAX SQL statement below returns the highest OrderPrice from the Sales table:

SELECT MAX(OrderPrice) FROM Sales

SQL Operators

3:20 PM Programming, SQL Commands 1 comment

SQL Operators

An operator is a reserved word or a character used primarily in an SQL statement's WHERE clause to perform operation(s).There are two type of Operators, namely

Comparison Operators
Logical Operators.

There are Operators That are used to specify conditions in an SQL statement and to serve as conjunctions for multiple conditions in a statement.

Arithmetic operators
Comparison operators
Logical operators
Operators used to negate conditions

SQL Arithmetic Operators:

Assume variable a holds 10 and variable b holds 20, then:

Operator	Description	Example
+	Addition - Adds values on either side of the operator	a + b will give 30
-	Subtraction - Subtracts right hand operand from left hand operand	a - b will give -10
*	Multiplication - Multiplies values on either side of the operator	a * b will give 200
/	Division - Divides left hand operand by right hand operand	b / a will give 2
%	Modulus - Divides left hand operand by right hand operand and returns remainder	b % a will give 0

Comparison Operators:

Comparison operators are used to compare the column data with specific values in a condition.

Comparison Operators are also used along with the SELECT statement to filter data based on specific conditions.

Assume variable a holds 10 and variable b holds 20, then:

Operator	Description	Example
=	Checks if the values of two operands are equal or not, if yes then condition becomes true.	(a = b) is not true.
!=	Checks if the values of two operands are equal or not, if values are not equal then condition becomes true.	(a != b) is true.
<>	Checks if the values of two operands are equal or not, if values are not equal then condition becomes true.	(a <> b) is true.
>	Checks if the value of left operand is greater than the value of right operand, if yes then condition becomes true.	(a > b) is not true.
<	Checks if the value of left operand is less than the value of right operand, if yes then condition becomes true.	(a < b) is true.
>=	Checks if the value of left operand is greater than or equal to the value of right operand, if yes then condition becomes true.	(a >= b) is not true.
<=	Checks if the value of left operand is less than or equal to the value of right operand, if yes then condition becomes true.	(a <= b) is true.
!<	Checks if the value of left operand is not less than the value of right operand, if yes then condition becomes true.	(a !< b) is false.
!>	Checks if the value of left operand is not greater than the value of right operand, if yes then condition becomes true.	(a !> b) is true.

SQL Logical Operators:

There are three Main Logical Operators namely, AND, OR, and NOT. These operators compare two conditions at a time to determine whether a row can be selected for the output. When retrieving data using a SELECT statement, you can use logical operators in the WHERE clause, which allows you to combine more than one condition.

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Here is a list of all the logical operators available in SQL.

Operator	Description
ALL	The ALL operator is used to compare a value to all values in another value set.
AND	The AND operator allows the existence of multiple conditions in an SQL statement's WHERE clause.
ANY	The ANY operator is used to compare a value to any applicable value in the list according to the condition.
BETWEEN	The BETWEEN operator is used to search for values that are within a set of values, given the minimum value and the maximum value.
EXISTS	The EXISTS operator is used to search for the presence of a row in a specified table that meets certain criteria.
IN	The IN operator is used to compare a value to a list of literal values that have been specified.
LIKE	The LIKE operator is used to compare a value to similar values using wildcard operators.
NOT	The NOT operator reverses the meaning of the logical operator with which it is used. Eg: NOT EXISTS, NOT BETWEEN, NOT IN, etc. This is a negate operator.
OR	The OR operator is used to combine multiple conditions in an SQL statement's WHERE clause.
IS NULL	The NULL operator is used to compare a value with a NULL value.
UNIQUE	The UNIQUE operator searches every row of a specified table for uniqueness (no duplicates).

ALL SQL COMANDS COLLECTION

3:03 PM Programming, SQL Commands 1 comment

Here is the collection of all sql commands. You can use this for recap and Quick study of SQL Commands.

This post consist of all SQL Commands with syntax and Functions for quick understanding.

This tutorial gives you a quick start with SQL by listing all the basic SQL Syntax:

All the SQL statements start with any of the keywords like SELECT, INSERT, UPDATE, DELETE, ALTER, DROP, CREATE, USE, SHOW and all the statements end with a semicolon (;).

Important point to be noted is that SQL is case insensitive which means SELECT and select have same meaning in SQL statements but MySQL make difference in table names. So if you are working with MySQL then you need to give table names as they exist in the database.

SQL SELECT Statement: