Automatic Text Extraction from a Cockpit Panel through Optical Character Recognition Algorithm using Color-Intelligent RGB to Black and White Conversion Method
Automatic Text Extraction from a Cockpit Panel through Optical Character Recognition Algorithm using Color-Intelligent RGB to Black and White Conversion Method |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-9 |
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| Year of Publication : 2024 | ||
| Author : Joseph Chakravarthi Chavali, D. Abraham Chandy |
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| DOI : 10.14445/22315381/IJETT-V72I9P102 | ||
How to Cite?
Joseph Chakravarthi Chavali, D. Abraham Chandy, "Automatic Text Extraction from a Cockpit Panel through Optical Character Recognition Algorithm using Color-Intelligent RGB to Black and White Conversion Method," International Journal of Engineering Trends and Technology, vol. 72, no. 9, pp. 18-32, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I9P102
Abstract
The functionality and failure conditions of Aircraft Systems are displayed by different indicator lights along with appropriate text messages on a Cockpit panel. The text messages from the Cockpit Panel will provide a clear picture of an aircraft system’s serviceability, malfunction, and failure. Thus, reading text messages can find the correct cause of an incident or accident in addition to a Flight Data Recorder (FDR). Character recognition of a text message is one of the major research areas in video analytics. The existing Optical Character Recognition (OCR) algorithm provides accurate character recognition in images, but its performance is not adequate for the analysis of video with different Vue and saturation conditions. The reflection and glare present in the video also degrade the performance of the OCR. Recent algorithms use the constant threshold and adaptive (Dynamic) threshold techniques depending on the overall brightness of the frame but fail to meet the accuracy requirements of real-time applications. In this paper, a Color-Intelligent conversion method is proposed, which converts the RGB Color frames into BW (black and white) images. This proposed method understands the Color information, provides channelised RGB to Gray conversion, and then converts it into a black-and-white format using the proper threshold value. This colour-based threshold technique of RGB to BW conversion enhances the character information during conversion. It provides accurate black-and-white image data to OCR to improve the overall accuracy of the text extraction.
Keywords
Black and White (BW) Image, Color-based Threshold Method, Constant Threshold Method, Dynamic Threshold Method, Flight Data Recorder (FDR), Gray Image, Optical Character Recognition (OCR).
References
[1] Reshma Deshmukh, and Anup Vibhute, “A Review on Digital Image Processing: Applications, Techniques and Approaches in Various Fields,” International Journal of Advanced Research, vol. 8, no. 6, pp. 726-734, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Basavaprasad Benchamardimath, and Ravindra S Hegadi, “A Study on the Importance of Image Processing and Its Applications,” International Journal of Research in Engineering and Technology, vol. 3, no. 1, pp. 1-6, 2014.
[Google Scholar]
[3] Chris Solomon, and Toby Breckon, Fundamentals of Digital Image Processing: A Practical Approach with Examples in MATLAB, Wiley Publishing, pp. 1-352, 2011.
[Google Scholar] [Publisher Link]
[4] Giovane R. Kuhn, Manuel M. Oliveira, and Leandro A. F. Fernandes, “An Improved Contrast Enhancing Approach for Color-to-Grayscale Mappings,” The Visual Computer, vol. 24, pp. 505-514, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Shayma Akram Hantush al-Rabii, and Ahmad Hasan Hamid, “Dynamic Weights Equations for Converting Grayscale Image to RGB Image,” Journal of the University of Babylon for Pure and Applied Sciences, vol. 26, no. 8, pp. 122-129, 2018.
[Google Scholar] [Publisher Link]
[6] Christopher Kanan, and Garrison W. Cottrell, “Color-to-Grayscale: Does the Method Matter in Image Recognition?,” PLoS One, vol. 7, no. 1, pp. 1-7, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Kavita Khobragade, “A Comparative Study of Converting Coloured Image to Gray-Scale Image Using Different Technologies,” National Conference on Recent Trends in Information Technology, Pune Maharashtra, India, pp. 1-4, 2012.
[Google Scholar]
[8] Tarun Kumar, and Karun Verma, “A Theory Based on Conversion of RGB Image to Gray Image,” International Journal of Computer Applications, vol. 7, no. 2, pp. 1-4, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Lina Zhang, Lijuan Zhang, and Liduo Zhang, “Application Research of Digital Media Image Processing Technology Based on Wavelet Transform,” EURASIP Journal on Image and Video Processing, vol. 2018, pp. 1-10, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Punam Mahesh Ingale, “The Importance of Digital Image Processing and its Applications,” International Journal of Scientific Research in Computer Science and Engineering, vol. 6, no. 1, pp. 31-32, 2018.
[Google Scholar] [Publisher Link]
[11] László Neumann, Martin Cadik, and Antal Nemcsics, “An Efficient Perception-based Adaptive Color to Gray Transformation,” Computational Aesthetics'07: Proceedings of the Third Eurographics Conference on Computational Aesthetics in Graphics, Visualization and Imaging, Goslar, Germany, pp. 73-80, 2007.
[Google Scholar] [Publisher Link]
[12] K. Padmavathi, and K. Thangadurai, “Implementation of RGB and Grayscale Images in Plant Leaves Disease Detection-Comparative Study,” Indian Journal of Science and Technology, vol. 9, no. 6, pp. 1-6, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Iliana Papamarkou, and Nikos Papamarkos, “Conversion of Color Documents to Grayscale,” 21st Mediterranean Conference on Control and Automation, Platanias, Greece, pp. 1609-1614, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[14] T. Prabaharan et al., “Studies on the Application of Image Processing in Various Fields: An Overview,” IOP Conference Series: Materials Science and Engineering, vol. 961, pp. 1-14, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Rafael C. Gonzalez, and Richard Eugene Woods, Digital Image Processing, 3rd ed., Prentice-Hall, USA, pp. 1-954, 2008.
[Google Scholar] [Publisher Link]
[16] Sithara Raveendran et al., “Design and Implementation of Reversible Logic based RGB to Gray scale Color Space Converter,” TENCON 2018 - 2018 IEEE Region 10 Conference, Jeju, Korea (South), pp. 1813-1817, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Beknazarova Saida Safibullaevna, Mukhamadiyev Abduvali Shukurovich, and Jaumitbayeva Mehriban Karamtdin kizi, “Processing Color Images, Brightness and Color Conversion,” 2019 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, pp. 1-5, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Samuel Macêdo, Givânio Melo, and Judith Kelner, “A Comparative Study of Grayscale Conversion Techniques Applied to SIFT Descriptors,” SBC Journal on Interactive Systems, vol. 6, no. 2, pp. 30-36, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[19] C. Saravanan et al., “Color Image to Grayscale Image Conversion,” 2010 Second International Conference on Computer Engineering and Applications, Bali, Indonesia, pp. 196-199, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Shaodi You, Nick Barnes, and Janine Walker, “Perceptually Consistent Color-to-Gray Image Conversion,” arXiv, pp. 1-18, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Wei Hong Lim, and Nor Ashidi Mat Isa, “A Novel Adaptive Color to Grayscale Conversion Algorithm for Digital Images,” Scientific Research and Essays, vol. 7, no. 30, pp. 2718-2730, 2012.
[Google Scholar] [Publisher Link]
[22] Yi Wan, and Qisong Xie, “A Novel Framework for Optimal RGB to Grayscale Image Conversion,” 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China, pp. 345-348, 2016.
[CrossRef] [Google Scholar] [Publisher Link]