An Effectual Optical Character Recognition Using Efficient Learning System

Abstract

There is an emerging attention for the various automatic classification frameworks to distinguish characters in software structure when information is looked over piece of paper records as it is noticed that the amount of daily identifications and records which are in published manner recognized with various businesses. This is popularly known as the document image exploration. To utilize Optical Character Recognition efficiently for character categorization so as to achieve image analysis, the data is utilizing in Grid assemblies. For high processing of documents, the heavy industries require a product as the framework which is known as character recognition classification. Also, there is need to create character recognition programming agenda to achieve document analysis in the form of image processing which deals with the organization of the material to the electronic reading arrangement. So this paper deals with the efficient learning approach which deals with the automatic classification from using processing of the images and machine learning for the optical character recognition process. In this research we have worked on the feature extraction using Independent component analysis with the swarm intelligence approach with Firefly algorithm, because it helps us to reduce more error probabilities and reduces the false positive and negative rates and increase the high learning rate. The learning is achieved using neural network. It is noticed that the proposed approach is able to perform high in terms of high specificity, sensitivity and recognition rate through which it is noticed that the proposed approach is able to achieve high true positive and true negative rates.