A Comparative Analysis for Optical Character Recognition for Text Extraction from Images Using Artificial Neural Network Fuzzy Inference System

A Comparative Analysis for Optical Character Recognition for Text Extraction from Images Using Artificial Neural Network Fuzzy Inference System Srikanth Bhyrapuneni Anandan Rajendran

Artificial neural networks (ANN) has the capability to analyze raw data from processing input-output relationships. This function considers them important in areas of industry with such information is unusual. Researchers have tried to extract the information embedded within ANNs as set of rules used with inference systems to resolve the black-box function of ANNs. When ANN applied within a fuzzy inference system, the extracted rules yield high classification accuracy. In this paper a Multi-Layer Neural Feed-Forward Network using Artificial Neural Network Fuzzy Inference System (MLNFFN-ANNFIS) is proposed for accurate character recognition from images. The technique targets areas of business that have less complicated issues about which there is no simpler approach is desired to a complex one. This paper proposed an Optical Character Recognition model for Text Extraction from Images using Artificial Neural Network Fuzzy Inference System for accurate text detection from images. The technique proposed is more effective and simple than most of the techniques previously proposed. The proposed model is compared with various traditional models and the results indicate that the proposed model accuracy is more and performance is also improved.
02 28 2022 02 28 2022 283 289 Crossmark v2.0 10.18280/CrossmarkPolicy www.iieta.org true 22 December 2021 23 January 2022 28 February 2022 http://iieta.org/sites/default/files/TEXT%20AND%20DATA%20MINING%20SERVICE%20AGREEMENT.pdf 10.18280/ts.390129 https://www.iieta.org/journals/ts/paper/10.18280/ts.390129 https://www.iieta.org/journals/ts/paper/10.18280/ts.390129