07-05-2013, 02:50 PM
A SEMINAR REPORT ON TAMIL CHARACTER RECOGNITION USING NEURAL NETWORKS
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ABSTRACT
Many of today’s document scanners for the PC come with the software that performs a task of character recognition. OCR software allows you to scan a printed document and then convert the electronic text in word format.
In our project we try to recognize characters of an Indian regional language. No such software is presently available in the market which is satisfactorily providing the required results. We scan a Tamil document and then recognize the characters using Neural Networks by extracting their features.
Today India is considered to be a huge market for all kinds of products. But many parts of the country are still not that well conversed with the English language. So to communicate with the people at grass root level or even the residents of that particular region, local or regional languages have to be used for communication. Thus to have the documentations in regional languages is the need of today. So after thorough discussions and analyzing the needs of the market we decided to make this project.
Though a very small part of digital image processing we find that the implementations of OCR in the corporate world are huge. It can be used in banking & other financial institutions, libraries, convert existing books into computer format so the books can be taken on a CD-ROM or directly uploaded on the internet.
FEATURES OF MATLAB
MATLAB is a highly productive development environment for creating scientific and engineering algorithms and applications, providing powerful tools for every step in the process. The high level language, interactive programming environment, built-in Math functions, editing and debugging tools, and deployment options all contribute to your overall development time.
With MATLAB you don’t have to develop algorithms from scratch or work with complicated interfaces to external libraries as is common with C and C++. You can write IMAGE PROCESSING algorithms in MATLAB just as you would express them mathematically. Most algorithms in the Image Processing toolbox are implemented in open MATLAB language, which allows you to learn from and customize existing algorithms.
Building applications around complex algorithms and graphics is easy with interactive GUIDE (Graphical User Interface Development Environment) tool, which lets you design and modify user interfaces with list boxes, pull-down menus, push buttons, radio buttons, and sliders as well as MATLAB plots and ActiveX controls. Alternatively, you can create GUIs programmatically using MATLAB functions.
You have several options for deploying your application. MATLAB lets you automatically convert your MATLAB programs into self-contained applications, and software components to share with end users. Applications and components created using the compiler do not require MATLAB to run. MATLAB builder for COM enables you to easily convert MATLAB algorithms to Common Object Model (COM) objects that are accessible from any com-based application.
MATLAB supports standard data and image formats, including JPEG, TIFF, PNG, HDF, HDF-EOS, FITS, XLS, ASCII and BINARY files. It also supports multiband image formats, such as LANDSAT. Low level input/output functions enable you to develop custom routines for working with any data format.
What IS Neural Network?
Neural networks are simplified models of biological nervous system. It is highly interconnected network of a large number of processing elements called neurons in an architecture inspired by the brain. A neural network can be massively parallel and therefore is said to exhibit parallel distributed processing.
Neural network exhibit characteristics such as mapping capabilities or pattern association, generalization, robustness, fault tolerance and parallel and high speed processing. Neural network learn by examples. They can therefore be trained with known examples of a problem to acquire knowledge about it.
Neural network adopts various learning mechanisms of which supervised learning and unsupervised learning methods have turned out to be very popular.
In supervised learning, a ‘teacher’ is assumed to be present during the learning process. That is the network aims to minimize the error between the targets (desired) output presented by ‘teacher’ and the computed output, to achieve better performance. However, in unsupervised learning, there is no teacher present to hand over the desired output and the network therefore tries to learn by itself, organizing the input of the problem.
Though NN architecture has been broadly classified as single layer feedforward networks, multilayer feedforward networks, and recurrent networks, over the years several other NN architectures have evolved. Some of the well known NN systems include backpropogation network, perceptron, ADALINE (Adaptive Linear Element), associative memory, Boltzmann machine, adaptive resonance theory, self-organizing feature map, and Hopfield network.
Neural network have been successfully applied to problems in the fields of pattern recognition, image processing, data compression, forecasting, and optimization to quote a few.
WORKING
The handwritten document is scanned using a scanner. The image of the scanned document is processed using the program. Initially each line is separated. Then the different words on each line are separated, and then each character in the word is isolated.
Then the individual isolated character is subjected to “Feature Extraction”. The features are the distinguishing characteristics of the letters, which are used to classify the letters into different classes. At the end of “Feature Extraction” every character has a Feature Vector assigned to it. This Feature Vector is a set of numbers corresponding to the characteristics of the letter.
Now this Feature Vector is given to the Neural Network. The Neural Network has two phases of operation
-Training phase
-Testing phase
Training phase
In this phase the Neural Network is trained by using the Feature Vector which is extracted from the characters. During training the network updates its weights according to the input pattern. At the end of training phase, the neural network reaches a steady state where its weights do not change. The weights attain a final value such that any pattern similar to those samples which are presented in the training phase can be recognized.
[attachment=53528]
ABSTRACT
Many of today’s document scanners for the PC come with the software that performs a task of character recognition. OCR software allows you to scan a printed document and then convert the electronic text in word format.
In our project we try to recognize characters of an Indian regional language. No such software is presently available in the market which is satisfactorily providing the required results. We scan a Tamil document and then recognize the characters using Neural Networks by extracting their features.
Today India is considered to be a huge market for all kinds of products. But many parts of the country are still not that well conversed with the English language. So to communicate with the people at grass root level or even the residents of that particular region, local or regional languages have to be used for communication. Thus to have the documentations in regional languages is the need of today. So after thorough discussions and analyzing the needs of the market we decided to make this project.
Though a very small part of digital image processing we find that the implementations of OCR in the corporate world are huge. It can be used in banking & other financial institutions, libraries, convert existing books into computer format so the books can be taken on a CD-ROM or directly uploaded on the internet.
FEATURES OF MATLAB
MATLAB is a highly productive development environment for creating scientific and engineering algorithms and applications, providing powerful tools for every step in the process. The high level language, interactive programming environment, built-in Math functions, editing and debugging tools, and deployment options all contribute to your overall development time.
With MATLAB you don’t have to develop algorithms from scratch or work with complicated interfaces to external libraries as is common with C and C++. You can write IMAGE PROCESSING algorithms in MATLAB just as you would express them mathematically. Most algorithms in the Image Processing toolbox are implemented in open MATLAB language, which allows you to learn from and customize existing algorithms.
Building applications around complex algorithms and graphics is easy with interactive GUIDE (Graphical User Interface Development Environment) tool, which lets you design and modify user interfaces with list boxes, pull-down menus, push buttons, radio buttons, and sliders as well as MATLAB plots and ActiveX controls. Alternatively, you can create GUIs programmatically using MATLAB functions.
You have several options for deploying your application. MATLAB lets you automatically convert your MATLAB programs into self-contained applications, and software components to share with end users. Applications and components created using the compiler do not require MATLAB to run. MATLAB builder for COM enables you to easily convert MATLAB algorithms to Common Object Model (COM) objects that are accessible from any com-based application.
MATLAB supports standard data and image formats, including JPEG, TIFF, PNG, HDF, HDF-EOS, FITS, XLS, ASCII and BINARY files. It also supports multiband image formats, such as LANDSAT. Low level input/output functions enable you to develop custom routines for working with any data format.
What IS Neural Network?
Neural networks are simplified models of biological nervous system. It is highly interconnected network of a large number of processing elements called neurons in an architecture inspired by the brain. A neural network can be massively parallel and therefore is said to exhibit parallel distributed processing.
Neural network exhibit characteristics such as mapping capabilities or pattern association, generalization, robustness, fault tolerance and parallel and high speed processing. Neural network learn by examples. They can therefore be trained with known examples of a problem to acquire knowledge about it.
Neural network adopts various learning mechanisms of which supervised learning and unsupervised learning methods have turned out to be very popular.
In supervised learning, a ‘teacher’ is assumed to be present during the learning process. That is the network aims to minimize the error between the targets (desired) output presented by ‘teacher’ and the computed output, to achieve better performance. However, in unsupervised learning, there is no teacher present to hand over the desired output and the network therefore tries to learn by itself, organizing the input of the problem.
Though NN architecture has been broadly classified as single layer feedforward networks, multilayer feedforward networks, and recurrent networks, over the years several other NN architectures have evolved. Some of the well known NN systems include backpropogation network, perceptron, ADALINE (Adaptive Linear Element), associative memory, Boltzmann machine, adaptive resonance theory, self-organizing feature map, and Hopfield network.
Neural network have been successfully applied to problems in the fields of pattern recognition, image processing, data compression, forecasting, and optimization to quote a few.
WORKING
The handwritten document is scanned using a scanner. The image of the scanned document is processed using the program. Initially each line is separated. Then the different words on each line are separated, and then each character in the word is isolated.
Then the individual isolated character is subjected to “Feature Extraction”. The features are the distinguishing characteristics of the letters, which are used to classify the letters into different classes. At the end of “Feature Extraction” every character has a Feature Vector assigned to it. This Feature Vector is a set of numbers corresponding to the characteristics of the letter.
Now this Feature Vector is given to the Neural Network. The Neural Network has two phases of operation
-Training phase
-Testing phase
Training phase
In this phase the Neural Network is trained by using the Feature Vector which is extracted from the characters. During training the network updates its weights according to the input pattern. At the end of training phase, the neural network reaches a steady state where its weights do not change. The weights attain a final value such that any pattern similar to those samples which are presented in the training phase can be recognized.