Offline Character Recognition System Using Artificial Neural Network
Nisha Vasudeva, Hem Jyotsana Parashar, Vijendra Pratap Singh · International Journal of Machine Learning and Computing · 2012
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.