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It develops an advanced technique to convert marathi inscription to marathi text. The purpose system will use machine learning techniques such as deep learning and advance language processing. The research involved collecting a large amount of marathi script input & outputting it to marathi text to guide the learning algorithm. Various machine learning models have been explored, Convolutional Neural Network (CNNs), and transform model to capture the data connection and detail’s required for accurate transformation. There are many OCR technologies on the market, but OCR technology is not effective in recognizing characters but more research is needed to identify text. Also challenging in recognition of marathiinscripts are discussed in this paper.

1. Definitely replace the marathi Inscription characters with their modern marathi equivalent

2. Insure accuracy & fairness in the transformation process

3. Handle variations & context-specific mapping inherent in marathiinscript

4. Develop an efficient & scalable algorithm for large volumes of text

5. Contribute to the preservation & promotion of the linguistic & cultural heritage of the marathi inscriptions

6. Converting Marathi inscript to Marathi text

A programme that can read characters from old Sinhala inscriptions using optical character recognition(OCR). The OCR module makes use of artificial neural network(ANN) and convolutional neural network(CNN) technology. The recognition rates using genius test photos, pre-processed test data, and from training are all included in the evaluation. The selected OCR solution in CNN since it performs better than ANN. CNN can only efficiently identify a character since there is not enough data[3].The article reviews methods for deciphering ancient marathi scripts from stone inscription, focuses on difficulties like background noise and script background similarly doesn’t provide any concerts finding but makes the case for future research on enhancing the precision of recognition[4].Abhishek Tomer, Minu Choudhary, Amit Yerpude[5] by comprehending the script and language in the photo, the author performed a survey investigation central on Indian inscription identification of characters areas and carried out prehistoric inscription analysis.Anush Goel and Akash Sehrawat [6] proposed a system that provides automatic data reading for blind people using Raspberry Pi. uses OCR technology to recognize printed characters using image detection devices and python programming.

Convert images to audio format using OCR and Text-to-Speech (TTS). The diagram for the conversion was made using a Raspberry Pi and the was reworked using the Tesseract library and python. Section Use the OpenCV library to process the text and complete the audio output.Shuping Liu, Yantian Xian, Huafeng Li, and Zentao Yu report a new detection model based on MCA and different representations. It provides two useful results for text search. The first is to develop the capacity to summarize and search for articles using the MCA method in summarizing. Article The method used for document search improves document search performance [7].Ancientdocumentsrecognition using statistical feature extraction techniques Preprocessing (Noise Removal, Binarization, etc) using local or global Thresholding Feature extraction CNN(88.95), section of the characters frame, including overlapping and non-overlapping zones, are divided up. Cone pixels may be used to calculate density, which is calculate as the whole number of pixels divided by the number of pixels in each zone[8].it is the next level of segmentation.

In their 2015 research, Sachin S. Bhat and H.V. Balachandra Achar sought to use cutting-edge identification algorithms to pinpoint the historical era of various ancient kannada scripts. Their technique included elements for image processing such character set segmentation, noise reduction, feature extraction, classification, and characters from inscriptions was an inventive element. Their MATLAB-based studies, which had an accuracy rate of 80%, proved the viability of their strategy[9].Soumya A and G Hemantha Kumar research focuses on improving the creation of ancient manuscripts using preprocessing and segmentation techniques. They convert scanned images into images in digital format. To enhance results, they present three filtering techniques: Gaussian blur, Laplacian filter, and USM filter. To get better result in terms of image quality and classification accuracy, these filters can be used in variety of combinations, including non-uniform filters[10].

Inscription :

The information history or information written on brickwork, stone and other brittle surfaces is call inscription. Indian Inscription may be categorised into four groups : stone, cave, pillar and wall. The written worlds on cave walls are called cave inscription. information written on originally stones is known as stone inscription. later inscription or pillar inscription were curved on artificially finished stone slabs, pillar and other stone materials.The figure 4 is one of the oldest known marathi inscription was unearthed at the foot of the Bahubali statue at the jain temple at Shravanabelagola.

Fig 4. Marathi inscription

Architecture  :

This section provides a succinct overview of the general structure of the architecture of converting marathiinscript to marathi text, as shown in figure 5. Which converts marathi in script to marathi text, which also stores Marathi text as output is stored in a text file(given a text file name).The architecture divided mainly into three part namely a. Pre-processing module, b.Text recognition module, and c. Post-processing module

Fig 5. Architecture of converting Marathiinscript to Marathi text

1. Pre-processing module : the pre-processing module is responsible for preparing the input image before it is fed into the text module. it’s main purpose is to improve image quality and improve OCR accuracy. Here are some preliminary steps are Image Resizing : Increase image size for better performance, Binary : simplify text extraction by converting images to binary format,Noise Reduction : Removes unused noise and imperfection in image that can be caused by imaging.

2. Text recognition module :The module for text recognition is the core of the OCR system. It will use the pre-processed image as input and perform correct character/word recognition. There are many ways to recognize text, including, Optical Character Recognition(OCR) : OCR algorithms analyze images to recognize & identify characters in text, Documents layout Analysis: understanding the layout of documents to determine the reading and distribution of documents.Modern text recognition after uses deep learning techniques such as convolutional neural network (CNNs) to achieve character recognition accuracy.

3. Post-processing module : the post-processing module takes the output recognition by the text module and further optimizes it to improve overall accuracy & fix anything not visible in the test at confirmation. Finishing operation include: Language model community, Context analysis, Confidence score.The post-processing module is required to solve OCR problem caused by various factors such as poor image quality, hard text, or invisible characters.

The user interface is developed by using Python as front end and OCR as backend where it enables computer to perform activities of reading Marathi inscription and convert it to equivalent Marathi Text as shown in figure 6, 7 and 8 respectively: Text detection process consist of Pre-processing:  The image concept is pre-processed to improve its quality and eliminate noise, which will improve the work quality of the next step,edge Detection: Use an edge detection technique to detect high contrast areas often associated with text borders. Text Region Suggestion: Use different methods such as floating windows or anchors to show regions containing text. Text Field Validation: These defined fields will be subjected to a validation process to determine if they contain text. This step usually involves classifying text and nontextual regions using machine learning or deep learning models.Text Localization: After verifying a region of text, a bounding box or pattern is drawn around the text to determine its position.

Among others, the development of smart algorithms to convert Marathi inscriptions into Marathi sentences represents a great achievement in preserving and deciphering the language and culture of Marathi inscriptions. Built on artificial intelligence, easyOCR and CNNs, the algorithm can translate historical Marathi inscript into Marathi text, paving the way for scholars, historians and language lovers to understand and understand ancient Marathi texts. The algorithm bridges the gap between the past and the present by addressing issues of translation, context-sensitive mapping and translation efficiency, unlocking the rich heritage of Marathi inscriptions and promoting a deeper understanding of the Marathi language and its historical origins.

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Marathi_Script_Recognition_from_Stone_Inscriptions

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(https://ijarcce.com/wp-content/uploads/2012/03/IJARCCE9G-s-soumya-Preprocessing-of-Camera.pdf)

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