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Epigraphic Document Image Enhancement Using Retinex Method

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Advances in Signal Processing and Intelligent Recognition Systems (SIRS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 678))

Abstract

Epigraphic Documents are the ancient handwritten text documents inscribed on stone, metals, wood and shell. They are the most authentic, solitary and unique documented evidences available for the study of ancient history. In the recent years, Archeological Departments worldwide have taken up the massive initiative of converting their repository of ancient Epigraphic Documents into digital libraries for the perennial purpose of their preservation and easy dissemination. The visual quality of the digitized Epigraphic Document images is poor as they are captured from sources that would have suffered from various kinds of degradations like aging, depositions and risky handling. Enhancement of these images is an essential prerequisite to make them suitable for automatic character recognition and machine translation. A new approach for enhancement of Epigraphic Document images using Retinex method is presented in this paper. This method enhances the visual clarity of the degraded images by highlighting the foreground text and suppressing the background noise. The method has been tested on digitized estampages of ancient stone inscriptions of 11th century written in old Kannada language. The results achieved are efficient in terms of root mean square contrast and standard deviation.

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Acknowledgement

We thank the organization, Archeological Survey of India(ASI), Mysore for providing access to their corpus of ancient Kannada inscription Estampages belonging to the Kalyani Chalukyan era of 11th century to conduct our research.

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Correspondence to H. T. Chandrakala .

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Chandrakala, H.T., Thippeswamy, G. (2018). Epigraphic Document Image Enhancement Using Retinex Method. In: Thampi, S., Krishnan, S., Corchado Rodriguez, J., Das, S., Wozniak, M., Al-Jumeily, D. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2017. Advances in Intelligent Systems and Computing, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-67934-1_16

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  • DOI: https://doi.org/10.1007/978-3-319-67934-1_16

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