Version 1
: Received: 24 December 2018 / Approved: 25 December 2018 / Online: 25 December 2018 (13:52:31 CET)
How to cite:
Mian Qaisa, S. Scene to Text Conversion and a Cymatics Based Configurable Text Perception. Preprints2018, 2018120306. https://doi.org/10.20944/preprints201812.0306.v1
Mian Qaisa, S. Scene to Text Conversion and a Cymatics Based Configurable Text Perception. Preprints 2018, 2018120306. https://doi.org/10.20944/preprints201812.0306.v1
Mian Qaisa, S. Scene to Text Conversion and a Cymatics Based Configurable Text Perception. Preprints2018, 2018120306. https://doi.org/10.20944/preprints201812.0306.v1
APA Style
Mian Qaisa, S. (2018). Scene to Text Conversion and a Cymatics Based Configurable Text Perception. Preprints. https://doi.org/10.20944/preprints201812.0306.v1
Chicago/Turabian Style
Mian Qaisa, S. 2018 "Scene to Text Conversion and a Cymatics Based Configurable Text Perception" Preprints. https://doi.org/10.20944/preprints201812.0306.v1
Abstract
This paper propose an original approach of achieving a Cymatics based visual perception of image-extracted text. In this context, an effective approach for automated text detection and recognition for the natural scene images is proposed. The incoming image is firstly enhanced by employing CLAHE and DWT. Afterwards, the text regions of the enhanced image are detected by employing the MSER feature detector. The non-text MSERs are removed by employing the geometrical and contour based filters. The remaining MSERs are grouped into words or phrases by finding out similarities between them. The text recognition is performed by employing an OCR function. The extracted text is sequentially analysed on character by character basis. Each character is converted into a methodical acoustic excitation. Finally, these excitations are converted into the systematic visual perceptions by using the phenomenon of Cymatics. The system functionality is tested with an experimental setup. For the case of studied natural scenes, the suggested approach achieves 80% precision in text localization and 53% precision in end-to-end text recognition. The devised system principle is novel and can be employed in various applications like visual art, encryption, education, integration of impaired people, etc.
Keywords
cymatics; text detection and recognition; optical character recognition (OCR)
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
