Recognition of Text Image Using Multilayer Perceptron
This work addresses the challenge of optical character recognition for converting scanned documents into editable digital text, but it appears incremental as it applies a standard backpropagation method to a common task.
The paper tackled the problem of recognizing printed and handwritten text in scanned documents by using a multilayer perceptron with backpropagation for character classification, achieving efficient recognition through error correction and feed-forward transmission in the neural network.
The biggest challenge in the field of image processing is to recognize documents both in printed and handwritten format. 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: The neural network is simulated in software on a digital computer. Character Recognition refers to the process of converting printed Text documents into translated Unicode Text. The printed documents available in the form of books, papers, magazines, etc. are scanned using standard scanners which produce an image of the scanned document. Lines are identifying by an algorithm where we identify top and bottom of line. Then in each line character boundaries are calculated by an algorithm then using these calculation, characters is isolated from the image and then we classify each character by basic back propagation. Each image character is comprised of 30*20 pixels. 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.