Abstract
A novel interactive technique for extraction of text characters from the images of stone inscriptions is introduced in this paper. It is designed particularly for on-site processing of inscription images acquired at various historic palaces, monuments, and temples. Its underlying principle is made of several robust character-analytic elements like HoG features, vowel diacritics, and location-bounded scan lines. Since the process involves character spotting and extraction of the inscribed information to editable text, it would subsequently help the archaeologists for epigraphy, transliteration, and translation of rock inscriptions, particularly for the ones having high degradations, noise, and a variety of styles according to the mason origin and reign. The spotted characters can also be used to create a database for ancient script analysis and related archaeological work. We have tested our method on various stone inscriptions collected from some of the heritage sites of Karnataka, India, and the results are quite promising. An Android application of the proposed work is also developed to aid the epigraphers in the study of inscriptions using a tablet or a mobile phone.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Patent pending: System and method for converting substrate inscription into electronically editable format; patent filing reference no.: 452/KOL/2014, April 2014.
References
Vamvakas, G., Gatos, B., Stamatopoulos, N., Perantonis, S.J.: A complete optical character recognition methodology for historical documents. In: International Workshop on Document Analysis Systems, pp. 525–532 (2008)
Kim, S.K., Sin, B.K., Lee, S.W.: Character spotting using image-based stochastic models. In: International Conference on Document Analysis and Recognition, pp. 60–63 (2001)
Barmpoutis, A., Bozia, E., Wagman, R.: A novel framework for 3d reconstruction and analysis of ancient inscriptions. Mach. Vis. Appl. 21, 989–998 (2010)
Galanopoulos, G., Papaodysseus, C., Arabadjis, D., Exarhos, M.: Exploiting 3D digital representations of ancient inscriptions to identify their writer. In: Bebis, G., et al. (eds.) ISVC 2012, Part II. LNCS, vol. 7432, pp. 188–198. Springer, Heidelberg (2012)
Shridevi, I.: Enhancement of inscription images. In: National Conference on Communications, pp. 1–5 (2013)
Mara, H., Hering, J., Kromker, S.: GPU based optical character transcription for ancient inscription recognition. In: International Conference on Virtual Systems and Multimedia, pp. 154–159 (2009)
Schmidt, N., Boochs, F., Schütze, R.: Capture and processing of high resolution 3D-data of Sutra inscriptions in China. In: Ioannides, M., Fellner, D., Georgopoulos, A., Hadjimitsis, D.G. (eds.) EuroMed 2010. LNCS, vol. 6436, pp. 125–139. Springer, Heidelberg (2010)
Papaodysseus, C., Rousopoulos, P., Arabadjis, D., Panopoulou, F., Panagopoulos, M.: Handwriting automatic classification: application to ancient greek inscriptions. In: International Conference on Autonomous and Intelligent Systems, pp. 1–6 (2010)
Papaodysseus, C., Rousopoulos, P., Giannopoulos, F., Zannos, S., Arabadjis, D., Panagopoulos, M., Kalfa, E., Blackwell, C., Tracy, S.: Identifying the writer of ancient inscriptions and Byzantine codices. A novel approach. Comput. Vis. Image Underst. 121, 57–73 (2014)
Rajakumar, S.: Eighth century Tamil consonants recognition from stone inscriptions. In: International Conference on Recent Trends in Information Technology, pp. 40–43 (2012)
Rousopoulos, P., Panagopoulos, M., Papaodysseus, C., Panopoulou, F., Arabadjis, D., Tracy, S., Giannopoulos, F., Zannos, S.: A new approach for ancient inscriptions’ writer identification. In: International Conference on Digital Signal Processing, pp. 1–6 (2011)
Shaus, A., Turkel, E., Piasetzky, E.: Binarization of first temple period inscriptions: performance of existing algorithms and a new registration based scheme. In: International Conference on Frontiers in Handwriting Recognition, pp. 645–650 (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: International Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: International Conference on Computer Vision and Pattern Recognition, pp. 60–65 (2005)
OpenCVDocumentation: (OpenCV documentation). http://docs.opencv.org/modulesimgprocdocobject_detection.html. Accessed 10 Nov 2013
Acknowledgement
This work is partially sponsored by Department of Science and Technology, Govt. of India through sanction number NRDMS/11/1586/2009.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Aswatha, S.M., Talla, A.N., Mukhopadhyay, J., Bhowmick, P. (2015). A Method for Extracting Text from Stone Inscriptions Using Character Spotting. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-16631-5_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16630-8
Online ISBN: 978-3-319-16631-5
eBook Packages: Computer ScienceComputer Science (R0)