Abstract—Advancement in Artificial Intelligence has lead to the developments of various “smart” devices. The biggest challenge in the field of image processing is to recognize documents both in printed and handwritten format. Character recognition is one of the most widely used biometric traits for authentication of person as well as document. Optical Character Recognition (OCR) is a type of document image analysis where scanned digital image that contains either machine printed or handwritten script input into an OCR software engine and translating it into an editable machine readable digital text format. A Neural network is designed to model the way in which the brain performs a particular task or function of interest. Each image character is comprised of 30×20 pixels. We have applied feature extraction technique for calculating the feature. Features extracted from characters are directions of pixels with respect to their neighboring pixels. These inputs are given to a back propagation neural network with hidden layer and output layer. We have used the Back propagation Neural Network for efficient recognition where the errors were corrected through back propagation and rectified neuron values were transmitted by feed-forward method in the neural network of multiple layers.
Index Terms—Back propagation algorithm, character recognition, multi-layer perceptron, supervised learning.
Vijendra Singh is with Department of Computer Science and
Engineering,Faculty of Engineering and Technology, Mody Institute of
Technology and Science, Lakshmangarh, Sikar, Rajasthan, India (email:
Hem Jyotsana Parashar and Nisha Vasudeva is with Department of Computer Science Engineering, Arya college of Engineering and IT, Kukas, Jaipur Rajasthan, India (email: email@example.com; firstname.lastname@example.org ).
Cite: Nisha Vasudeva, Hem Jyotsana Parashar and Singh Vijendra, "Offline Character Recognition System Using Artificial Neural Network," International Journal of Machine Learning and Computing vol. 2, no. 4, pp. 449-452, 2012.