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Optical Character Recognition Using Back Propagation Neural Network

Shyla Afroge, Md. Abu Raihan, Firoz Mahmud

Abstract


This paper represents an Artificial Neural Network (ANN)-based approach for the recognition of English characters using feed-forward neural network. Noise has been considered as one of the major issues that degrades the performance of character recognition system. Our feed forward network has one input, one hidden, and one output layer. The entire recognition system is divided into two sections, namely training and recognition section. Both the sections include image acquisition, preprocessing and feature extraction. Training and recognition section also include training of the classifier and simulation of the classifier, respectively. Preprocessing involves digitization, noise removal, binarization, line segmentation and character extraction. After character extraction, the extracted character matrix is normalized into 12x8 matrix. Then features are extracted from the normalized image matrix which is fed to the network. The network consists of 96 input neurons and 62 output neurons. We train our network by proposed training algorithm in a supervised manner and establish the network. Eventually, we have tested our trained network with more than 10 samples per character and gives 99% accuracy for numeric digits (0~9), 97% accuracy for capital letters (A~Z), 96% accuracy for small letters (a~z) and 93% accuracy for alphanumeric characters by considering inter-class similarity measurement.

Cite this Article
Afroge S, Raihan MA, Mahmud F. Optical Character Recognition Using Back Propagation Neural Network. Journal of Image Processing & Pattern Recognition Progress. 2016; 3(2): 11–18p.


Keywords


optical character recognition (OCR), character recognition, BPN network, English alphanumeric characters, image acquisition

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References


Kader MF, Deb K. Neural network based English alphanumeric character recognition. IJCSEA. 2012; 2(4): 1–10p.

Bell R, Jackson T. Neural Computing: An Introduction. Munich: Siemens; 2004.

Kakkar P, Dutta U. A novel Approach to Recognition of English Characters Using Artificial Neural Networks. IJAREEIE. 2014; 3(6): 10238–45p.

Prasad K, Nigam DC, Lakhotiya A, et al. Character Recognition Using Matlab’s Neural Network Toolbox. International Journal of u- and e- Service, Science and Technology. 2013; 6(1): 13–20p.

Mollah AF, Majumder N, Basu S, et al. Design of an Optical Character Recognition System for Camera based Handheld Devices. IJCSI. 2011; 8(4): 283–9p.

Pal A, Singh D. Handwritten English Character Recognition Using Neural Network. Int J Comp Sci Comm. 2010; 1(2): 141–4p.

Gonzalez RC, Woods RE, Eddins SL. Digital Image Processing Using MATLAB, Fifth Impression. USA: Gatesmark Publishing; 2012.

Pathak A. Restoration of Documents With Show-through Distortion. M.Sc Thesis. Ontario, Canada: University of Ottawa; 2000.

Jung K, Kim KI, Jain AK. Text Information Extraction in Images and Video: A Survey. Pattern Recognition. 2004; 37(5): 977–97p.

Prasad BK, Sanyal G. A Model Approach to Off-line English Character Recognition. International Journal of Scientific and Research Publications. 2012; 2(6): 1–6p.

Verma R, Ali J. A-Survey of Feature Extraction and Classification Techniques in OCR Systems. Int J Com Appl Info Technol. 2012; 1(3): 1–3p.


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