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The inscriptions have contributed for Kannada literature and helps to classify it as Old Kannada,Old Kannada,Old Kannada,intermediate kannada and New Kannada.Different techniques can be used to create this technique,but we need strong ideas to create better systems than the existing ones.Kannada was established during the Mauryan period, which dates back to the 3rd century BC.It is generally considered to be prehistoric Kannada.over the rule of various dynasty from Ganga to Rashta Kutas toChalukyas,"Halegannada" slowly transformed into "Nadugannada" and saw changes in the scenario.The"Nadugannada"of the Hoysalas,Vijayanagar and Mysore dynasties underwent many changes over a period of five centuries and is now classified as"Hosagannada".In the following research paper Rajithkumar B K1,Dr.H.S.Mohana,used “Template Matching Method for Recognition of Stone Inscripted Kannada Characters of Different Time Frames based on Correlation Analysis” on June [2014]

[1]Effective communication training achieves the goal of producing better translations that provi-de better accuracy,for Kannada inscription to hosagannda text is a ground breaking initiative that aims to preserve and unlock the linguistic and cultural treasures embedded within Kannada inscript literature. Kannada inscript, an ancient writing system, poses significant challenges for comprehension and accessibility due to variations, complex character mappings, and linguistic context. This algorithm harnesses the power of artificial intelligence and machine learning to accurately translate kannada inscript into modern Kannada, enabling researchers, historians, and language enthusiasts to decipher and explore the wealth of knowledge and cultural heritage contained within these inscript texts. By bridging the gap between the past and the present, this algorithm offers a transformative solution for understanding and preserving the rich legacy of Kannada inscript.

we hope the datasets launched as part of this undertaking will make contributions definitely toward forwarding studies inside the system translation of underneath-resourced languages.

1.1 Terminologies and notations

Based on the various research paper the following article “A Survey of Deep Learning Techniques for Neural Machine Translation” by  S Yang [ 2020][1] addressed the methods and technologies of DNN(Deep neural network)

Natural language processing (NLP) combines computational modeling (modeling of human language) with mathematics, machine learning,and deep learning models.

1. Natural language processing is the driving force behind machine intelligence in many applicati--ons today.

2. Natural language learning is a language with many subfields, such as artificial intelligence (AI),

3. Machine learning (ML), and Deep learning (DL) such as deep neural networks (DNN).

4. Deep learning (DL) is a subset of ML that consists of algorithms trained to mimic the human brain and perform tasks such as pattern recognition.

Fig.3 Deep Neural Network(DNN) subfield of Deep-Learning(DL)

2.1. Advent to the review

This method addresses the role of knowledge division  in cognitive behavior of research.

Optical Character Recognition (OCR) software from Google [2015] https://opensource.com/life/15/9/open-sourceextract-text-images: is an example of an OCR workflow.

Use Kannada notation to show complex results after analysis.

2.2. Inscription analyzing guide techniques  and Use of OCR

The OCR is used to recognize and translate ancient various Kannada inscription languages. The OCR module contains the technologies of  Machine Learning  algorithm and Deep-Neural Network(DNN),high accuracy optical character recognition has been report in the prose for nonIndic language such as English, which have very small characters and fewer structural problems.By research review in the paper article Samuel Lukas, Ridvi avyodri “Optical Character Recognition (OCR) for Text Recognition and its Post-Processing Method: A Literature Review” has approached the methods on 14 November [2022][1].Although there are currently several conventional OCR methods for Kannada text,R. Mahesh K. Sinha said in a research article titled "Journey from Hindi Word Processing to Indian Language Processing"in The One Month [2009] [2] that there are almost no studies to do.It was published many years ago in the field of analysis and interpretation of ancient texts,which is a more difficult field than the translation of texts into their current form.

In the research paper Amruta B.Patil,J.A.shaikh,has used OTSU Thresholding Method for Flower Image Segmentation, on [May 2016] Available at:http://www.ijceronline.com/papers/Vol6_issue5/A06 50106.pdf

DNN Kannada Inscription To Text

Input: Old Kannada Inscript Image

Output: Translating into Kannada text

Hale-gannadaa Lipi Data storage(Ashoka-Era)1)

2)

3)4)5)

Fig 4. Architecture of a Deep Neural Network (DNN) Machine-Translation

3.1 Facts series with the aid of Preprocessing the input image.

1.1 Test engine KOW values for compounds and simple molecular pipeline input system strings are written to be suitable for building QSPR models.

The string here is sufficient to represent the information of the molecular structure.

3.2  Function extraction via person areas from the preprocessed photograph.

2.1

The concatenated string is used to create a list of number vectors according to the proposed atomic insertion algorithm used by atomic signature. Vectors define molecular structures and represent their properties.

 

“Comparing the OCR Accuracy Levels of Bitonal and Greyscale Images” [online] available at http://www.dlib.org/dli b/march09/powell/03powell.html techniques are referenced and applied for the following kannada inscripts Translation from halegannada  To hosagannada text.

3.4 Information processing.

3.1

These sequences were transformed into classical molecular signatures using classical molecular graph theory [58]. Based on this, these features are tuned into a tree-LSTM network whose purpose is to  generate vector as input.

3.5  Version training.

4.1.After receiving the input from the TreeLSTM network, BPNN supports the matching process and works backwards to learn the QSPR model of interest. During the training process, the parameters are adjusted to take advantage of the disadvantages of the DNN model,and finally the performance of the SQSPR model is recorded for KOW prediction.

3.5  Performance evaluation.

5.1.According to the QSPR model, general ability is evaluated by estimating the quality of external data. And evaluate the exterior competition of the QSPR form by comparing it with the official estimate model.       

3.6  Output the converted Kannada Text.

quit set of rules

4.1.1) The Vikarama Era

4.1.2) The Saka Era

4.1.3) The Gupta- Valabhi Era

The development of machine translation is a way of converting sentences from one language to another with the help of computer systems,Our machine translation system for old kannada Inscript to modern Kannada offers highly accurate translations between halegannada and hosagannada, very quickly, and at no cost to users.All the above mentioned kannada old inscripts  provide  image to text  translation ,our some of developed dataset  from image processing to  Text Translation System that has addressed  various old inscripts (Halegannada) to hosagannada Text translation through OCR(optical character recognization),and Deep Neural Network(DNN) method Of Machine Translation algorithm.

Output is as shown below:

Fig.6 Detection of Kannada Shasana for obtaining Output        

Output: Halmidi Shasana

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2. P. Bannigidad and C. Gudada, "Restoration of degraded Kannada handwritten paper inscriptions (Hastaprati) using image enhancement techniques," 2017 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, pp. 1-6, (2017).

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