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\title{Digitization and Enhancement of Ancient Marathi Inscription Images Using Wavelet-Based Techniques}

\author{\authorname{Bapu D. Chendage\sup{1}\orcidAuthor{0000-0001-8882-9363}, Rajivkumar S. Mente\sup{1}\orcidAuthor{0009-0007-1696-919X} and Vikas J Magar\sup{2}\orcidAuthor{0000-0002-1055-4088}}
\affiliation{\sup{1a}Department of Computer Science, Central University of Karnataka, Kadaganchi, Karnataka, India}
\affiliation{\sup{1b}Department of Computer Applications, P.A. H. Solapur University, Solapur, Maharashtra, india}
\affiliation{\sup{2}School of Computer Science, Dr. Vishwanath Karad MIT World Peace University Pune, Maharashtra, india}
\email{\{bapuchendage@cuk.ac.in, rsmente@sus.ac.in, magarvjresearch@gmail.com\}}
}


\keywords{Wavelet Transform, Ancient Inscriptions, Image Enhancement, Noise Reduction, PSNR, MSE.}

\abstract{Ancient stone inscriptions are important cultural and historical artifacts, but their readability often suffers from environmental damage, erosion, and wear over time. This study introduces a wavelet-based image enhancement algorithm designed to improve the clarity and legibility of ancient Marathi inscriptions. The proposed method includes converting inscription images to grayscale, adding Gaussian noise for performance testing, and applying multi-level 2D Discrete Wavelet Transform (DWT) followed by soft or hard thresholding to reduce noise while keeping the structural features intact.
Experimental validation took place on a dataset made up of images from archaeological texts, expert consultations, and field visits to historical sites in Maharashtra. A quantitative assessment using PSNR, MSE, SSIM, and FSIM showed that the method works well, with an average PSNR of 30.99 dB, SSIM of 0.72, and FSIM of 0.81. These results confirm that the algorithm effectively reduces noise while keeping important details of the inscriptions, making it a useful tool for digital preservation and epigraphic analysis. The study also points out that soft thresholding is better at achieving visually smoother and structurally consistent improvements. Overall, this approach provides a practical and strong solution for restoring damaged inscription images in archaeological and heritage contexts.
	}

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\section{\uppercase{Introduction}}
\label{sec:introduction}

Ancient stone inscriptions are an important link to our cultural, historical, and linguistic heritage. They offer valuable insights into the past. However, over the years, many of these inscriptions, especially those in places like Maharashtra and written in ancient Marathi, have suffered significant damage. This damage is due to environmental exposure, weathering, biological growth, and human interference. As a result, the readability of these inscriptions is often poor. This creates challenges for historians, linguists, and archaeologists trying to preserve, interpret, or digitize them accurately \cite{chendage2021study}.\\
Traditional image improvement techniques often struggle to find a balance between removing noise and keeping important features. This is especially true for intricately carved, low-contrast stone inscriptions. To tackle this problem, this work presents a wavelet-driven method for restoration and clarity enhancement designed specifically for ancient Marathi stone inscriptions \cite{chendage2021survey}. The approach uses multi-level 2D Discrete Wavelet Transform (DWT) to break the image into frequency sub-bands. This allows for selective noise removal while keeping the edges and textures that define the inscription's structure.
\begin{figure}[!h]
	\centering
	{\epsfig{file = im1.jpg, width = 8cm}}
	\caption{Block diagram of wavelet based denoising.}
	\label{fig:example1}
\end{figure}
The Figure 1 shows the process of wavelet-based image denoising through a step-by-step flowchart. It begins with a noisy input image, which undergoes a wavelet transform to separate the image into different frequency components. A threshold is then estimated and applied using either soft or hard thresholding to suppress noise in the wavelet domain. Finally, an inverse wavelet transform reconstructs the image, resulting in a denoised output that retains important features while reducing unwanted noise \cite{baleanuWaveletBook}. This method is particularly effective for enhancing historical or degraded images, such as ancient stone inscriptions.
\section{\uppercase{Literature Review}}
Recent years have seen increased interest in wavelet-based and hybrid image enhancement methods for cultural heritage, medical, and scientific fields. While specific studies on Marathi stone inscriptions are limited, broader literature shows significant progress relevant to this work.

\cite{zhao2025} presented an improved wavelet soft-threshold function that integrates a dual-parameter joint denoising technique for monitoring ancient building deformation. Their method enhances sensitivity to signal changes in wavelet detail components, showing notable adaptability in heritage contexts.

\cite{li2025} investigated speckle noise removal in OCT medical imaging by combining wavelet transform with deep learning (DnCNN). This hybrid approach demonstrates the rising trend of improving denoising performance through wavelet-domain processing along with neural networks .

\cite{rao2025} introduced a new shrinkage function in wavelet shrinkage denoising. This function performs traditional techniques like Wiener filtering in RMSE and PSNR, showcasing ongoing innovation in wavelet thresholding methods.

\cite{young2023} presented a Bayesian wavelet restoration framework that uses multifractal priors. Their method models complex textures and multiscale features of images directly in wavelet space, providing adaptive enhancement. This is particularly useful for preserving inscriptions with varying carving depths and surface textures.

\cite{chandrakala2018} utilized the Retinex method along with morphological operations to improve degraded epigraphic document images. The work is based on  improving clarity, especially in low-contrast inscription settings.

\cite{ju2022} developed a three-stage binarization method that combines DWT and GANs for degraded document images. Their design uses both wavelet decomposition and deep learning to enhance text capture in multichannel document images. T

Historical reviews \cite{benharzallah2023} of document binarization show the effectiveness of adaptive wavelet thresholding techniques, especially for correcting ink bleed and improving contrast. This validates the suitability of wavelet methods for heritage image restoration .

\cite{kalyani2020} discussed multispectral imaging techniques. This method captures inscriptions using UV and NIR bands to extract details that are not visible under standard visible-light imaging. While not directly wavelet-based, this strategy could greatly improve input quality before wavelet enhancement.

Advanced deep learning methods in manuscript restoration, such as those for ancient Arabic texts, combine Wiener filters, CLSR, and CNN-based techniques \cite{prochazkaWaveletRestoration}. These show how traditional filtering can be integrated with neural networks for robust recovery.

The existing research highlights the effectiveness of wavelet thresholding for image denoising, particularly in preserving fine structural details like edges and textures \cite{mohamed2012face}. Improved techniques, including adaptive thresholding and Bayesian wavelet models, have shown better performance in managing different noise levels and image degradations. Additionally, hybrid techniques that merge wavelet transforms with deep learning architectures have appeared as promising solutions for enhancing complex or severely degraded images \cite{baleanuWaveletBook} \cite{suryanath2015multiscale}\cite. Furthermore, multispectral imaging and specialized filters provide unique advantages in pre-processing and feature extraction, especially in archaeological imaging.
\section{\uppercase{Proposed Methodology}}
The wavelet transform measures time and frequency around specific points. It works best with non-stationary signals. This transform gives good resolution for low-frequency devices and high time resolution for high-frequency devices. 
The DWT is a signal processing method that breaks a signal into a set of mutually orthogonal wavelet basis functions. First, we estimate thresholds for all expansion coefficients. When we apply the threshold at each level, we create a denoising matrix for all feature points at that level \cite{chendage2022restoration}. We use these matrices as coefficients in the inverse discrete wavelet transform to rebuild the image. The rebuilt image is denoised.
\subsection{Algorithm: Enhance And Visualize Wavelet Decomposition}
{\textbf{Input:}}\\
    - AncientImage ← Input ancient inscription image\\
    - ThresholdType ← 'hard' or 'soft'\\
    - WaveletType ← e.g., 'db4', 'haar'\\
    - Levels ← 3 (Number of decomposition levels=9)\\
{\textbf{Output:}}\\
- EnhancedImage\\
- Visualization of decomposition levels and enhanced result\\

\begin{small}
	\begin{verbatim}
	Begin:
1. Load AncientImage
2. Convert AncientImage to GrayscaleImage
3. Add Gaussian noise to GrayscaleImage
    → NoisyImage
Else:
NoisyImage ← GrayscaleImage
4. Choose ThresholdType:
If user_input == 'hard' then use 
   HardThresholding
Else use SoftThresholding
5. Perform 2D DWT on NoisyImage up to Levels:
Coeffs ← DWT2D(NoisyImage, WaveletType, Levels)
6. For each level i from 1 to Levels:
Extract:
LH_i ← Horizontal detail coefficients
HL_i ← Vertical detail coefficients
HH_i ← Diagonal detail coefficients
LL_i ← Approximation coefficients (only 
       for highest level)
Display or store LH_i, HL_i, HH_i (and optionally
       LL_i) as visual output
7. For each set of detail coefficients
       (LH_i, HL_i, HH_i):
Compute threshold T_i using a method 
      (e.g., universal threshold)
Apply ThresholdType (hard/soft) to coefficients
8. Reconstruct EnhancedImage using Inverse DWT:
EnhancedImage ← IDWT2D(Coeffs, WaveletType)
9. Display:
- Original GrayscaleImage
- NoisyImage
- Decomposed sub-bands (LH, HL, HH) for each level
- Final EnhancedImage
10. Compute image quality metrics:
- PSNR ← ComputePSNR(GrayscaleImage, 
         EnhancedImage)
- SSIM ← ComputeSSIM(GrayscaleImage,
         EnhancedImage)
- MSE ComputeMSE(GrayscaleImage, EnhancedImage)
- SSIM ComputeSSIM(GrayscaleImage, EnhancedImage)
- Display metrics value
End
	\end{verbatim}
\end{small}
\subsection{Image Acquisition}
Image acquisition is the first step in the proposed wavelet-based enhancement algorithm. It involves getting high-quality digital images of ancient Marathi stone inscriptions. These images are the starting point for further processing. The image acquisition process includes:\\
\textbf{Source of Images}\\
For the proposed methodology, inscription images were collected from three main sources:  \\
• Consultation with archaeologists from Solapur and Pune.  \\
• Archaeology-related books, journals, and printed materials that included high-resolution reproductions of stone inscriptions.\\  
• Field visits to historically significant sites in the districts of Solapur, Pune, Nashik, and Dharashiv, where on-site photographs of ancient Marathi inscriptions were taken \cite{chendage2021study}.  \\
  \textbf{Image Format:} JPG or JPEG\\
  \textbf{Grayscale Conversion}\\
  After acquiring the inscription images, the next step in the proposed methodology is grayscale conversion. This step is important because it simplifies the image data by reducing it from three color channels (RGB) to one intensity channel \cite{chendage2023enhancement}. This process does not significantly compromise the structural details that are essential for improvement.\\
\textbf{Addition of Gaussian Noise to Grayscale Image}
To simulate the real-world degradation commonly found in historical stone inscriptions, Gaussian noise is added to the grayscale image as part of the image preprocessing stage \cite{chendage2022comparative}. This step is essential for testing and evaluating the effectiveness of the proposed wavelet-based enhancement and denoising algorithm.\\
\textbf{Threshold Type Selection}\\
An important part of the proposed wavelet-based enhancement algorithm is choosing the right thresholding method. This choice is crucial for reducing noise while keeping the structural features of ancient inscription images intact \cite{yang2011licenseplate}\\
In wavelet-based denoising, thresholding is used on the detail coefficients, which are the high-frequency components from the Discrete Wavelet Transform (DWT). These coefficients often contain both fine details of the inscriptions and unwanted noise \cite{chacko2012handwritten}.\\ Thresholding helps remove less important coefficients, which are considered noise, while preserving the significant ones that reflect the actual image content, such as edges or carved characters.\\
There are two primary types of thresholding techniques:\\
1. Hard Thresholding\\
Hard thresholding sets all coefficients whose absolute value is below a certain threshold to zero, and leaves the rest unchanged.\\
\[
T_H(x) = \begin{cases}
x & \text{if } |x| \geq \lambda \\
0 & \text{if } |x| < \lambda
\end{cases}
\]\\

\textbf{where:}
\begin{itemize}
	\item $T_H(x)$: \textbf{Hard thresholding function} applied to $x$
	\item $x$: Input value (can be scalar or part of a vector)
	\item $\lambda$: Threshold parameter (a positive constant)
	\item $|x|$: Absolute value of $x$
\end{itemize}

2. Soft Thresholding\\
Soft thresholding not only sets small coefficients to zero but also shrinks the larger ones toward zero.\\
\[
T_S(x) = \begin{cases}
\operatorname{sign}(x)(|x| - \lambda) & \text{if } |x| \geq \lambda \\
0 & \text{if } |x| < \lambda
\end{cases}
\]\\
\textbf{Where:}
\begin{itemize}
	\item $T_S(x)$: \textbf{Soft thresholding function} applied to $x$
	\item $x$: Input value (can be scalar or part of a vector)
	\item $\lambda$: Threshold parameter (a positive constant)
	\item $|x|$: Absolute value of $x$
	\item $\operatorname{sign}(x)$: Sign function, returns $1$ if $x>0$, $-1$ if $x<0$, and $0$ if $x=0$
\end{itemize}
\textbf{2D Discrete Wavelet Transform (DWT)}\\
The 2D DWT breaks down the input image into multiple sub-bands representing different spatial frequency components and orientations. This decomposition allows selective processing (such as thresholding) on the detail coefficients—those components representing edges, textures, and noise—while preserving the approximation coefficients that contain the main image structure \cite{ju2022}. Performing the transform up to multiple levels enables a multi-scale analysis, capturing image features at varying resolutions\\
At each level of decomposition, the image is split into four sub-bands using a pair of low-pass and high-pass filters applied along rows and columns:\\
LL (Approximation coefficients): Low-frequency components capturing the overall image structure.\\
LH (Horizontal details): High-frequency components capturing horizontal edges.\\
HL (Vertical details): High-frequency components capturing vertical edges.\\
HH (Diagonal details): High-frequency components capturing diagonal details.\\
The LL sub-band can be further decomposed in subsequent levels to analyze finer details.\\
\textbf{Multi-level Decomposition}\\
In the proposed method, a 3-level decomposition is applied.
At each level, the LL component is recursively decomposed to obtain progressively coarser approximations and finer detail coefficients \cite{mahato2019}.\\
This multi-scale representation enables targeted noise suppression at different frequency bands and scales, improving enhancement accuracy.\\
After applying thresholding to the detail coefficients from wavelet decomposition, the Inverse Discrete Wavelet Transform (IDWT) is performed to reconstruct the improved image \cite{mohamed2012face}. This step is essential for turning the processed wavelet coefficients back into the spatial domain, creating a denoised and sharpened version of the original inscription image. \\
After reconstructing the improved image using the inverse discrete wavelet transform, it is important to measure the quality of the enhancement \cite{mohamed2012face}. Image quality metrics offer objective measures that evaluate how well the denoising and enhancement steps preserved the original information while reducing noise.
\section{Experimental Results and Discussion}
The proposed wavelet-based enhancement method was thoroughly tested on a varied dataset of ancient Marathi stone inscription images. These images were collected from three sources: consultations with archaeologists, archaeological literature, and field visits to the districts of Solapur, Pune, Nashik, and Dharashiv. The images, which were initially captured or scanned in high resolution, were converted to grayscale and had controlled noise added for algorithm validation. Figures 2 and 3 show the improved outputs of inscription images obtained through wavelet denoising using the sigthresh thresholding method. Figure 4 presents the stages of wavelet decomposition after applying the proposed method.\\
\begin{figure}[!h]
	\centering
	{\epsfig{file = im2.jpg, width = 8cm}}
	\caption{Enhanced Output of Inscription Image 2 Using Wavelet Denoising with sigthresh Method.}
	\label{fig:example1}
\end{figure}
\\
\\
\\
\begin{figure}[!h]
	\centering
	{\epsfig{file = im3.jpg, width = 8cm}}
	\caption{Enhanced Output of Inscription Image 1 Using Wavelet Denoising with sigthresh Method.}
	\label{fig:example1}
\end{figure}

\begin{figure}[!h]
	\centering
	{\epsfig{file = im4.jpg, width = 8cm}}
	\caption{ Stages of Wavelet Decomposition Demonstrated After Applying the Proposed Method.}
	\label{fig:example1}
\end{figure}

\begin{table}[h!]
	\centering
	\caption{Image quality metrics: PSNR, MSE, SSIM, and FSIM}
	\begin{tabular}{|c|c|c|c|c|}
		\hline
		\textbf{Image} & \textbf{PSNR} & \textbf{MSE} & \textbf{SSIM} & \textbf{FSIM} \\
		\hline
		Img1  & 32.15 & 0.18 & 0.78   & 0.89 \\
		Img2  & 28.82 & 0.27 & 0.74   & 0.80 \\
		Img3  & 23.53 & 0.38 & 0.70   & 0.81 \\
		Img4  & 26.56 & 0.32 & 0.76   & 0.85 \\
		Img5  & 21.80 & 0.40 & 0.63   & 0.72 \\
		Img6  & 28.86 & 0.26 & 0.76   & 0.82 \\
		Img7  & 34.96 & 0.12 & 0.80   & 0.91 \\
		Img8  & 36.74 & 0.13 & 0.82   & 0.93 \\
		Img9  & 27.96 & 0.24 & 77.95* & 0.79 \\
		Img10 & 31.62 & 0.19 & 0.76   & 0.80 \\
		\vdots & \vdots & \vdots & \vdots & \vdots \\
		I100  & 39.04 & 0.11 & 0.88   & 0.96 \\
		\hline
		\textbf{AVG} & \textbf{30.99} & \textbf{0.24} & \textbf{0.72} & \textbf{0.81} \\
		\hline
	\end{tabular}
	
	\label{tab:image_metrics}
\end{table}
The table 1 presents the quantitative evaluation of the proposed wavelet-based enhancement algorithm applied to a set of ancient Marathi inscription images. The key metrics used for assessment include PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Squared Error), SSIM (Structural Similarity Index Measure), and FSIM (Feature Similarity Index Measure).\\
\\
\\
The algorithm's multi-level wavelet decomposition and adaptive thresholding effectively reduce noise in grayscale images while preserving important details of ancient inscriptions \cite{prochazkaWaveletRestoration}. The average PSNR of about 31 dB shows strong noise suppression, while the average SSIM and FSIM values of approximately 0.72 and 0.81 demonstrate the algorithm's effectiveness in maintaining structural and feature-level information. Images with lower scores usually reflect more heavily damaged inscriptions or higher noise levels, highlighting the difficulties in restoring archaeological images.
\section{Conclusion}
This work presents a wavelet-based image enhancement algorithm designed to improve the readability and clarity of ancient Marathi stone inscriptions. These inscriptions often face degradation from erosion, environmental exposure, and age. The proposed method involves grayscale conversion, Gaussian noise simulation, multi-level 2D Discrete Wavelet Transform (DWT), and soft or hard thresholding. This approach effectively removes noise while preserving important features of the inscriptions. 
The experimental evaluation of the algorithm on a diverse set of inscription images shows its strength and effectiveness. Key image quality metrics, including Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM), were calculated to assess the enhancement process.\\
The average PSNR value of 30.99 dB shows that the algorithm significantly reduces noise while maintaining a close resemblance to the original image data. The low average MSE of 0.24 confirms minimal deviation from the original grayscale images, validating the accuracy of the reconstruction process. The SSIM value of 0.72 and FSIM of 0.81 indicate that the algorithm retains critical structural and feature information needed for epigraphic analysis, like the edges, curves, and shapes of the engraved text. Visual analysis of the wavelet decomposition also shows that the detail coefficients (LH, HL, HH) effectively isolate high-frequency noise, allowing for targeted thresholding without losing inscription details. Among the tested methods, soft thresholding consistently produced smoother and more visually natural results compared to hard thresholding, as evidenced by better PSNR and SSIM values for inscription images.

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