Artificial Neural Network / Hand written character Recognition

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1. Overview
2.Development of System
3.GCR Model
4.Proposed model
5.Back ground Information
6. Preprocessing
7.Architecture
8.ANN(Artificial Neural Network)
9.How the Human Brain Learns?
10.Synapse
11.The Neuron Model
12.A typical Feed-forward neural network model
13.The neural Network
14.Training of characters using neural networks
15.Regression of trained neural networks
16.Training state of neural networks
17.Graphical user interface….

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Artificial Neural Network / Hand written character Recognition

  1. 1. “RECOGNITION OF CHARACTERS &DIGITS” “Recognition of Assamese Vowels Consonants and Digits” Presented By: Sri Uday Saikia(Roll no. xxxxxx
  2. 2. PROJECT OVERVIEW: one of the challenging computational processes.  There is competition between the speed and efficiency.  The human mind can easily decipher these handwritten characters easily, accurately and speedily.  The human mind can do it because of the presence of densely neural network in his mind. 
  3. 3. DEVELOPMENT OF RACD SYSTEM  The problem defines in the acquisition process of an RACD system can be justified by training of neural networks in reconstruction of Assamese characters & Digits. First of all, the system by offline handwritten different shapes of Assamese characters is taught. On the basis of this image model database, character sets are matched and classify the reconstructed image.
  4. 4. GCR MODEL
  5. 5. BACK GROUND INFORMATION
  6. 6. PREPROCESSING
  7. 7. ARCHITECTURE OF RACD CHARACTERS AND DIGITS) (RECOGNITION OF ASSAMESE
  8. 8. ANN(ARTIFICIAL NEURAL NETWORK)  1. Biological Neuron
  9. 9. HOW THE HUMAN BRAIN LEARNS?  Components of a neuron
  10. 10.  Synapse
  11. 11.  The Neuron Model
  12. 12. A typical Feed-forward neural network model
  13. 13. THE NEURAL NETWORK
  14. 14. Training of characters using neural networks
  15. 15.  Regression of trained neural networks
  16. 16. Training state of neural networks
  17. 17. GRAPHICAL USER INTERFACE….

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