Abstract
Many systems have been designed to fulfill the requirements of the society. OCR is providing a platform to illiterate or visually impaired persons by converting handwritten/Printed document in digitized format automatically. Many researchers worked on the Marathi OCR, but still require lot of improvement as per the user’s requirement. Many algorithms have been developed for image processing. Nature inspired computing uses the base of the nature to get the optimized result for the problems e.g. humans, insects, nature, animal behavior. The main purpose of this paper is to provide the knowledge of the techniques including the traditional and nature inspired optimization algorithm proposed by the researchers in area of image processing. We aim to provide advantages and disadvantages of the traditional and nature inspired methods, which would help researchers to select the specific method for the applications.
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Trier I, Jain AK, Torfinn T (1996) Feature extraction methods for character recognition—a survey. Pattern Recognit 29(4):641–662
Vamvakas G, Gatos B, Stamatopoulos N, Perantonis SJ (2008) A complete optical character recognition methodology for historical documents. In: The eighth IAPR workshop on document analysis systems 2008 IEEE. 978-0-7695-3337-7/08
Seethalakshmi R, Sreeranjani TR, Balachandar T (2005) Optical character recognition for printed Tamil text using unicode. J Zhejiang Univ Sci 6A(11):1297–1305
Jundale TA, Hegadi RS (2015) Skew detection and correction of Devanagari script using hough transform. Procedia Comput Sci 45(2015):305–311
Ntirogiannis K, Gatos B, Pratikakis I, Senior Member IEEE (2013) Performance evaluation methodology for historical document image binarization. IEEE Trans Image Process 22(2):1057–7149
Cheriet M, Nawwaf K, Liu CL, Suen CY (2007) Character recognition systems a guide for students and practioners. Wiley, New York. ISBN 978-0-471-41570-1
Tuba M, Bacanin N, Alihodzic A (2015) Multilevel image thresholding by fireworks algorithm. In: Radioelektronika (RADIOELEKTRONIKA), 2015 25th international conference. 978-1-4799-8117-5
Roy A, Bhowmik TK, Parui SK, Roy U (2005) A novel approach to skew detection and character segmentation for handwritten Bangla words. In: Digital imaging computing: techniques and applications DICTA IEEE 2005. 0-7695-2467-2/05
Liu X, Cao Z, Ai K, Jiao J, Tan M (2014) A general image skew detection approach with a bio-inspired mechanism. In: Proceeding of the 11th world congress on intelligent control and automation Shenyang, China, June 29–4 July. 978-1-4799-5825-2/14
Bansal V, Sinha RMK (1998) Segmentation of touching characters in Devanagari. In: Proceedings CVGIP Delhi, pp 371–376
Pan Y, Zhou T, Xi Y (2015) Bacterial foraging based edge detection for cell image segmentation. In: Engineering in medicine and biology society (EMBC), 2015 37th annual international conference of the IEEE 25–29 Aug 2015
Dawson L, Stewart IA (2014) Accelerating ant colony optimization-based edge detection on the GPU using CUDA. In: 2014 IEEE congress on evolutionary computation (CEC), Beijing, China, July 6–11. 978-1-4799-1488-3/14
Aslam A, Khan E, Beg MMS (2015) Multi-threading based implementation of ant-colony optimization algorithm for image edge detection. IEEE INDICON 2015. 978-1-4673-6540-6/15
Choudharya A, Rishib R, Ahlawat S (2013) A new character segmentation approach for off-line cursive handwritten words. In: Information technology and quantitative management (ITQM2013) Procedia Computer Science, vol 17, pp 88–95
Yang XS (2014) Nature-inspired optimization algorithms. Elsevier Science Publishers, Amsterdam, pp 141–154
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Deokate, S., Uke, N. (2018). Various Traditional and Nature Inspired Approaches Used in Image Preprocessing. In: Pawar, P., Ronge, B., Balasubramaniam, R., Seshabhattar, S. (eds) Techno-Societal 2016. ICATSA 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-53556-2_34
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DOI: https://doi.org/10.1007/978-3-319-53556-2_34
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