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Image-Abstraction Framework as a Preprocessing Technique for Extraction of Text From Underexposed Complex Background and Graphical Embossing Images

Image-Abstraction Framework as a Preprocessing Technique for Extraction of Text From Underexposed Complex Background and Graphical Embossing Images
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Author(s): Pavan Kumar (Jawaharlal Nehru New College of Engineering, India), Poornima B. (Bapuji Institute of Engineering and Technology, India), H. S. Nagendraswamy (University of Mysore, India), C. Manjunath (Jawaharlal Nehru National College of Engineering, India) and B. E. Rangaswamy (Bapuji Institute of Engineering and Technology, India)
Copyright: 2021
Volume: 13
Issue: 1
Pages: 35
Source title: International Journal of Distributed Artificial Intelligence (IJDAI)
Editor(s)-in-Chief: Firas Abdulrazzaq Raheem (University of Technology - Iraq, Iraq) and Israa AbdulAmeer AbdulJabbar (University of Technology - Iraq, Iraq)
DOI: 10.4018/IJDAI.2021010101

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Abstract

Underexposed heterogeneous complex-background and graphical embossing text documents are treated using proposed preprocessing image-abstraction framework that can deliver the effective structure preserved abstracted output by manipulating visual-features from input images. Reading of the text character in such images is extremely poor; hence, the framework effectively boosted the significant image properties and quality features at every stage. Work effectively preserves the foreground structure of an image by comprehensively integrating the sequence of NPR filters and diminishes the background content of an image, and in this way, the framework contributes to separation of foreground text from image background. Effectiveness of the proposed work has been validated by conducting the trials on the selected dataset. In addition, user's visual-feedback and image quality assessment techniques were also used to evaluate the framework. Based on the obtained abstraction output, this work extracts text-character by wisely utilizing traditional image processing techniques with an average accuracy of 98.91%.

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