Ancient Tamil Characters Research Articles

Self-Adaptive Hybridized Lion Optimization Algorithm With Transfer Learning for Ancient Tamil Character Recognition in Stone Inscriptions

Tamil character recognition is an important field of research in pattern recognition and it is a technical challenge than other languages due to similarity and complexity of characters. Stone inscriptions reveal the details of lavishness, lifestyle, economic conditions, culture, and also of the managerial regulations followed by various rulers and dynasties particular to those regions. However, due to the long history of ancient stone inscription, natural erosion and lack of early protection measures, there are lot of noise in the existing ancient stone inscriptions, which create adverse effects on reading these stone inscriptions and their aesthetic appreciation. The research challenge in recognizing Tamil characters is mainly because of the characters with a number of holes, loops and curves. The number of letters in Tamil language is higher when compared to other languages. Even though there are various approaches provided by the researchers, challenges and issues still prevail in recognition of tamil text in stone inscriptions. In the existing systems, detection algorithms fail to produce desired accuracy and hence stone inscription recognition using transfer learning, a promising method is proposed in this paper. Lion Optimization Algorithm (LOA) is applied to optimize brightness and contrast and then stone inscription images are pre-processed for noise removal and then each character is separated by identifying contours. Characters are recognized using Transfer Learning (TL), a Deep Convolution Neural Network-based multi classification approach. The proposed hybrid model Self-Adaptive Lion Optimization Algorithm with Transfer Learning (SLOA-TL) when implemented in images of stone inscriptions achieves better accuracy and speed than other existing methods. It serves as an efficient design for recognition of tamil characters in stone inscriptions and preserving tamil traditional knowledge.

Recognition of Ancient Tamil Characters from Epigraphical inscriptions using Raspberry Pi based Tesseract OCR

Optical Character Recognition (OCR) is the process of identification of the printed text using photoelectric devices and computer software. It converts the inscribed text on the stones into machine encoded format. OCR is widely used in machine learning process like cognitive computing, machine translation, text to speech conversion and text mining.OCR is mainly used in the research fields like Character Recognition, Artificial Intelligence and Computer Vision. In this research, the recognition process is done using OCR, the inscribed character is processed using Raspberry Pi device on which it recognizes characters using Artificial Neural Network. This work mainly focuses on the recognition of ancient Tamil characters inscribed on stones to modern Tamil characters belong to 9th and 12th century characters. The input image is subjected to gray scale conversion process and enhanced using adaptive thresholding process. The output image is subjected to thinning process to reduce the pixel size of the image. Then the characters are classified using Artificial Neural Network Architecture and the classified characters are mapped to modern Tamil character using Unicode. The Artificial Neural Network has input layer, hidden layer of 15 neurons and output layer of 1 neuron to classify the characters. The accuracy of the constructed system for the recognition of epigraphical inscriptions is calculated. The above process is carried out in raspbian environment using python process.

Creation of original Tamil character dataset through segregation of ancient palm leaf manuscripts in medicine

AbstractPalm leaves were one of the essential and primary sources for writing before the advent of paper. Tamil is one of the oldest southern Indian languages and among the ten oldest languages of the world. Agathiyar, a renowned Siddhar of ancient India, considered as the Father of Siddha Medicine, wrote all his therapeutic procedures only on palm leaf manuscripts, in the Tamil language. For modern day, people who try to only write and read new aspects of Tamil, identifying the ancient characters of the language is difficult. To expand readability and secure the written medicinal practices and traditions, a better recognition system is needed that can transform the ancient text images to modern ones, interpreting the ancient Tamil characters from palm leaves and understanding their contexts a time‐consuming and complicated process. Especially when it comes to medicine, the practitioners need to understand the contents of a manuscript, to apply them on a daily basis. Therefore, a recognition system is of much use to understand, interpret, and apply the techniques explained in the manuscript on a daily basis. This study is an attempt to create a considerable volume of Tamil character datasets through the segregation of ancient Tamil palm leaf manuscripts related to the field of medicine. In this study, the characters created are fed as inputs to expert systems for intelligent recognition of the context and content perceived to be present in the selected medical manuscripts. The characters have been identified in large numbers manually, and datasets are created using Gaussian distortion.

Soft computing approaches for character credential and word prophecy analysis with stone encryptions

The uniform corpus of untranslated script is a preliminary stage for computational epigraphy. Mechanizing this process through deep learning algorithms will be an essential support to the epigraphical research. Our proposed system based on soft computing techniques focuses on the progression of recognizing the eleventh-century ancient Tamil character and converting them into current-century word form. Initially, the system is implemented by performing preprocessing steps followed by image segmentation. The decomposed image undergoes a hybrid feature extraction technique along with Chi-square test to check whether entire pixel in image of Zernike is bounded inside the unit circle or not, whereas ANOVA method is used for testing the significant difference between HOG feature and zoning feature. These functions are subjected to image classification and proceeded with character recognition using convolutional neural networks. Finally, the identified character is progressed into word form with the help of boggle algorithm. The hybrid feature extraction along with convolutional neural networks is achieved with 92.78% of recognition rate accurately. Our experiment shows a large perspective of deep learning algorithms in automatic epigraphy.

Accurate recognition of ancient handwritten Tamil characters from palm prints for the Siddha medicine systems

The ancient Tamil characters recognition is the complex task because there is no sufficient training information is available. Various researchers attempted to perform accurate recognition of ancie...

Recognizing Ancient Characters from Tamil Palm Leaf Manuscripts using Convolution Based Deep Learning

Palm leaf manuscripts has been one of the ancient writing methods but the palm leaf manuscripts content requires to be inscribed in a new set of leaves. This study has provided a solution to save the contents in palm leaf manuscripts by recognizing the handwritten Tamil characters in manuscripts and storing them digitally. Character recognition is one of the most essential fields of pattern recognition and image processing. Generally Optical character recognition is the method of e-translation of typewritten text or handwritten images into machine editable text. The handwritten Tamil character recognition has been one of the challenging and active areas of research in the field of pattern recognition and image processing. In this study a trial was made to identify Tamil handwritten characters without extraction of feature using convolutional neural networks. This study uses convolutional neural networks for recognizing and classifying the Tamil palm leaf manuscripts of characters from separated character images. The convolutional neural network is a deep learning approach for which it does not need to retrieve features and also a rapid approach for character recognition. In the proposed system every character is expanded to needed pixels. The expanded characters have predetermined pixels and these pixels are considered as characteristics for neural network training. The trained network is employed for recognition and classification. Convolutional Network Model development contains convolution layer, Relu layer, pooling layer, fully connected layer. The ancient Tamil character dataset of 60 varying class has been created. The outputs reveal that the proposed approach generates better rates of recognition than that of schemes based on feature extraction for handwritten character recognition. The accuracy of the proposed approach has been identified as 97% which shows that the proposed approach is effective in terms of recognition of ancient characters.

ACCURATE RECOGNITION OF ANCIENT HANDWRITTEN TAMIL CHARACTERS FROM PALM PRINTS FOR THE SIDDHA MEDICINE SYSTEMS

The ancient Tamil characters recognition is the complex task because there is no sufficient training information is available. Various researchers attempted to perform accurate recognition of ancient Tamil characters. In our preceding work, hybrid multi-neural learning based prediction and recognition system (HMNL-PRS) is introduced for the prediction process which lacks from inaccurate recognition. In this proposed research work, this is overcome by proposing the Brahmi character prediction and conversion system (BC-PCS) methodology. Here, the modified graph based segmentation algorithm (MGSA) is used to segment the characters. And then the statistical and structural features are extracted based on which classification is done using hybridised support vector machine based fuzzy neural network. In the MATLAB simulation environment, the proposed research work is implemented and it is confirmed that the proposed research work direct to give the excellent result compared to the preceding research methodology in terms of recognition rate.

12th Century Ancient Tamil Character Recognition From Temple Wall Inscriptions

i-manager's Journal on Future Engineering and Technology, i-manager's Journal on Software Engineering, i-manager's Journal on Electrical Engineering, i-manager's Journal on Electronics Engineering, i-manager's Journal of Educational Technology, i-manager's Journal on Educational Psychology, i-manager's Journal on School Educational Technology, i-manager's Journal on English Language Teaching, i-manager's Journal on Mechanical Engineering, i-manager's Journal on Civil Engineering, i-manager's Journal on Embedded Systems, i-manager's Journal on Information Technology, i-manager's Journal on Structural Engineering, i-manager's Journal on Communication Engineering and Systems, i-manager's Journal on Wireless Communication Networks, i-manager's Journal on Instrumentation and Control Engineering, i-manager's Journal on Circuits and Systems, i-manager's Journal on Digital Signal Processing, i-manager's Journal on Power Systems Engineering, i-manager's Journal on Computer Science, i-manager's Journal on Material Science, i-manager's Journal on Cloud Computing, i-manager's Journal on Image Processing, i-manager's Journal on Mobile Applications and Technologies, i-manager's Journal on Pattern Recognition, i-manager's Journal on Mathematics, i-manager's Journal on Management, i-manager's Journal on Nursing, i-manager's Journal on Physical Sciences, i-manager's Journal on Chemical Sciences, i-manager's Journal on Economics & Commerce , i-manager's Journal on Humanities & Social Sciences, i-manager's Journal on Life Sciences , i-manager's Journal on Construction Management, i-manager's Oyla , ,India best Journal Publication Website,World Best Plication SIte,Free atrticle published website,Journal Publised,Published Journal,Research Paper Published,indian research paper publications,how to published your article/research paper in india for free,journal’s instructions for authors

Century Identification and Recognition of Ancient Tamil Character Recognition

Century Identification and Recognition of Ancient Tamil Character Recognition