WITHDRAWN: An energy efficient and improved language translator with cnn based deep encoder and decoder

Abstract

Abstract The attempt to identify the text in natural language processing is considered to a confronting task based on the variations encountered on the language formats like textures, fonts and illuminations. English is a common language used on Arab countries with Arabic scripts. To provide better understanding towards the tough Arabic language; this work introduces an English-Arabic text recognition with the available dataset. With the available data evaluation has been made for recognizing the text and provide with a proper meaning. To show an extensive research perspective to the investigators, experimentation is carried over the text features and classification is done with Deep learning approaches. This work presents a novel approach to extract invariant features from the input data and to detect appropriate work. So as to choose appropriate features, weight of the invariant feature is determined and provided for validation. Here, an encoder and a decoder are placed with the layer of CNN to perform computation. The work has been compared with existing approaches SVM, NB classifier to project the advantages of the proposed model. Simulation is done with MATLAB environment and the proposed model shows better trade off in identifying the machine based language translation for analyzing text.