Abstract
The study and analysis of epigraphy is important for knowing about the past. From around third century to modern times, about 90,000 inscriptions have been discovered from different parts of India.
This chapter is based on the conference papers published in proceedings of NCC 2013 (IEEE explore) and ICVGIP 2014 (ACM digital library).
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References
Salomon R (1998) Indian epigraphy. A guide to the study of inscriptions in Sanskrit, Prakrit, and the other Indo-Aryan languages. Oxford University Press
Verghese A, Dallapiccola, AL (eds) (2011) South India under Vijayanagara, art and archaeology. Oxford University Press
Hyvarinen A, Karhunen J, Oja E (2004) Independent component analysis, vol 46. Wiley
Sreedevi I et al (2013) Enhancement of inscription images. In: 2013 National Conference on Communications (NCC). IEEE. “978-1-4673-5952-8/13/$ 31.00 2013 IEEE”
Sreedevi I et al (2013) Ngfica based digitization of historic inscription images. ISRN Signal Process 2013:7, Article ID 735857. http://dx.doi.org/10.1155/2013/735857
Jayanthi N et al (2014) Digitization of historic inscription images using cumulants based simultaneous blind source extraction. In: Proceedings of the 2014 Indian conference on computer vision graphics and image processing. ACM. “Copyright 2014 ACM 978-1-4503-3061-9/14/12 ...$15.00 http://dx.doi.org/10.1145/2683483.2683534”
Amari S-I, Cichocki A, Yang HH (1996) A new learning algorithm for blind signal separation. Adv Neural Inf Process Syst 757–763
Amari S, Douglas S (2001) Why natural gradient? Brain Style Information Systems Group, Japan
Nery MS et al (2005) Determining the appropriate feature set for fish classification tasks. In: XVIII Brazilian symposium on computer graphics and image processing (SIBGRAPI’05). IEEE
Optical character recognition. http://www.onlineocr.net
Cichocki A, Amari S-I (2002) Adaptive blind signal and image processing: learning algorithms and applications, vol 1. Wiley
Tonazzini Anna, Bedini Luigi, Salerno Emanuele (2004) Independent component analysis for document restoration. Doc Anal Recogn 7(1):17–27
Cruces-Alvarez SA, Cichocki A, Amari S-I (2004) From blind signal extraction to blind instantaneous signal separation: criteria, algorithms, and stability. IEEE Trans Neural Netw 15(4):859–873
Cruces-Alvarez Sergio A, Cichocki Andrzej, Amari Shun-Ichi (2002) On a new blind signal extraction algorithm: different criteria and stability analysis. IEEE Signal Process Lett 9(8):233–236
Katsumata Naoto, Matsuyama Yasuo (2005) Database retrieval for similar images using ICA and PCA bases. Eng Appl Artif Intell 18(6):705–717
Huber PJ (1985) Projection pursuit. In: The annals of statistics, pp 435–475
Blaschke Tobias, Wiskott Laurenz (2004) CuBICA: Independent component analysis by simultaneous third-and fourth-order cumulant diagonalization. IEEE Trans Signal Process 52(5):1250–1256
Otsu Nobuyuki (1975) A threshold selection method from gray-level histograms. Automatica 11(285-296):23–27
Garainl, et al (2008) Machine reading of camera-held low quality text images: an ICA-based image enhancement approach for improving OCR accuracy. In: 2008 19th International Conference on Pattern Recognition, ICPR 2008. IEEE
Pratikakis I, Gatos B, Ntirogiannis K (2010) H-DIBCO 2010-handwritten document image binarization competition. In: 2010 international conference on frontiers in handwriting recognition (ICFHR). IEEE
Pratikakis I, Gatos B, Ntirogiannis K (2013) ICDAR 2013 document image binarization contest (DIBCO 2013). In: 2013 12th international conference on document analysis and recognition (ICDAR). IEEE
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This work is an output of DST-funded Project IDH. This work would not have been completed without the help of Ayush, Aman, Rishi Pandey and Geetanjali Bhola.
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Sreedevi, I., Natarajan, J., Chaudhury, S. (2017). Processing of Historic Inscription Images. In: Mallik, A., Chaudhury, S., Chandru, V., Srinivasan, S. (eds) Digital Hampi: Preserving Indian Cultural Heritage. Springer, Singapore. https://doi.org/10.1007/978-981-10-5738-0_15
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