Call for Paper - May 2015 Edition
IJCA solicits original research papers for the May 2015 Edition. Last date of manuscript submission is April 20, 2015. Read More
Text based Image Recognition using Multilayer Perceptron
IP Multimedia Communications
© 2011 by IJCA Journal
ISBN : 978-93-80864-99-3
Year of Publication: 2011
Dharm Singh, Neha Mehta and Pallavi Purohit. Text Based Image Recognition using Multilayer Perceptron. Special issues on IP Multimedia Communications (1):143-146, October 2011. Full text available. BibTeX
@article{key:article,
author = {Dharm Singh and Neha Mehta and Pallavi Purohit},
title = {Text Based Image Recognition using Multilayer Perceptron},
journal = {Special issues on IP Multimedia Communications},
month = {October},
year = {2011},
number = {1},
pages = {143-146},
note = {Full text available}
}
Abstract
The ultimate objective of any OCR is to simulate the human reading capability. Optical Character Recognition, usually abbreviated to OCR, is the mechanical or electronic translation of images of handwritten, typewritten or printed text into machine. Character Recognition refers to the process of converting printed Text documents into translated Unicode Text. The printed documents are scanned using standard scanners which produce an image of the scanned document. Lines are identified by an algorithm where we identify top and bottom of line and in each line character boundaries are calculated by an algorithm, using these calculation characters are isolated from the image and then we will classify each character by basic back propagation. The back propagation algorithm works by what is known as supervised training. It first submits an input for a forward pass through the network. The network output is compared to the desired output, which is specified by a "supervisor" and the error for all the neurons in the output layer is calculated. The fundamental idea behind back propagation is that the error is propagated backward to earlier layers so that a gradient descent algorithm can be applied. Each image character is comprised of 30×20 pixels. In this paper we have used the Back propagation Neural Network for efficient recognition where the errors were corrected and rectified neuron values were transmitted by feed-forward method in the neural network of multiple layers.Reference
- Andrew Blais and David Mertz, “An Introduction to Neural Networks Pattern Learning with Back Propagation Algorithm”, Gnosis Software, Inc., July 2001
- David Bouchain,"Character Recognition Using Convolutional Neural Networks", Seminar Statistical Learning Theory University of Ulm, Germany Institute for Neural Information Processing Winter 2006/2007
- Anita Pal1 and Dayashankar Singh, Handwritten English Character Recognition Using Neural Network”,Department of Computer Science & Engineering, U.P.Technical University, Lucknow, India International Journal of Computer Science & CommunicationVol. 1, No. 2, July-December 2010, pp. 141-144
- Omaima N.A. AL-Allaf, “Improving the Performance of Backpropagation Neural Network Algorithm for Image Compression/Decompression System”,Department of Computer Information Systems,Faculty of Sciences and Information Technology, AL-Zaytoonah Private University of Jordan, P.O. Box 130, Amman (11733), Jordan Journal of Computer Science 6 (11): 1347-1354, 2010
- Raghuraj Singh, C. S. Yadav, Prabhat Verma, Vibhash Yadav,"Optical Character Recognition (OCR) for Printed Devnagari Script Using Artificial Neural Network",International Journal of Computer Science & Communication Vol. 1, No. 1, 2010, pages 91-95.
- Christopher M.Bishop”Neural Networks For Pattern Recognition” Department of computer Science and applied mathematics Aston University , Birmingham, UK Clarndon press, 1995.
- Dong Xiao Ni Seidenberg,” Application of Neural Networks to Character Recognition”, CSIS, Pace University, May 4th, 2007, School of CSIS, Pace University, White Plains, NY.
- Dayashankar Singh, Maitreyee Dutta and Sarvpal H. Singh, “Neural Network Based Handwritten Hindi Character Recognition”, ACM International Conference (Compute 09), Jan. 9-10, 2009, Bangalore.
- Hongxi Wei Guanglai Gao, "Machine-Printed Traditional Mongolian Characters Recognition Using BP Neural Networks ",Sch. of Comput. Sci., Inner Mongolia Univ., Hohhot, China,1 - 7,Dec. 2009.
- Sutha.J, Ramraj.N, “Neural Network Based Offline Tamil Handwritten Character Recognition System”, IEEE International Conference on Computational Intelligence and Multimedia Application, 2007, 2, 13-15, Dec.2007, Page(s),446-450, 2007.