Classification of Handwritten Tamil Characters in Palm Leaf Manuscripts Using SVM Based Smart Zoning Strategies

Sabeenian, R. S.; Paramasivam, M.; Dinesh, P. M.; Adarsh, R; Kumar, Gokul Ravi

doi:10.1145/3133793.3133804

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…Sabeenian et al [15] evaluated localized binarization techniques on Tamil palm leaf scripts, which were recorded to hold a huge quantity of info that is highly useful in everyday life. To protect the data on the palm, digital pictures of each leaf were evaluated and preserved.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts

Subramaniyaswamy

Teekaraman

et al. 2022

Computational Intelligence and Neuroscience

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Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem. In this research, a unique Convolutional Neural Network (CNN) technique is utilized to train the characteristics of the palm leaf characters. By this training, CNN can classify the palm leaf characters significantly on training phase. Initially, a preprocessing technique to remove noise in the input image is done through morphological operations. Text Line Slicing segmentation scheme is used to segment the palm leaf characters. In feature processing, there are some major steps used in this study, which include text line spacing, spacing without obstacle, and spacing with an obstacle. Finally, the extracted cursive characters are given as input to the CNN technique for final classification. The experiments are carried out with collected cursive Tamil palm leaf manuscripts to validate the performance of the proposed CNN with existing deep learning techniques in terms of accuracy, precision, recall, etc. The results proved that the proposed network achieved 94% of accuracy, where existing ResNet achieved 88% of accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts

Subramaniyaswamy

Teekaraman

et al. 2022

Computational Intelligence and Neuroscience

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“…The study relied on a national mission and collected recognitions for the manuscripts. Sabeenian et al (2017) proposed an appraisal of localized binarization methods on Tamil palm leaf manuscripts. The palm leaf manuscripts were documented to have large amounts of data, which is very important for daily life.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Subramani

Subramaniam²

2020

Expert Systems

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Palm 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. K E Y W O R D S ancient Tamil characters, expert systems for character recognition, Gaussian distortion, palm leaf manuscripts in Tamil, Tamil and medicine, Tamil medical manuscripts 1 | INTRODUCTION Tamil is one of the oldest and renowned languages in the entire world with a strong literature domain. In the prehistoric period, the poets, particularly in Tamil Nadu, used palm leaves to conceal information. The ancient literature integrates several palm leaf works that include Sangam literature, masterpieces, Saiva, Vaishnava, medical works, cuisine, astrology, Vaastu, jewels, songs, dance and drama, and Siddha (Vellingiriraj and Balasubramanie, 2013; Vellingiriraj and Balasubramanie, 2014). In the last decade, the importance of the ancient medical manuscripts in Tamil and attention towards the need to restore them have attracted the interest of many researchers. The preserved ancient medical manuscripts in Tamil by saints such as Agathiyar, were created for the first time, through a digitization process in a seminar hosted in 2006 by Foundation for Revitalisation of Local Health Traditions. Thereafter, the National Mission for Manuscripts has engaged in active research to preserve medical documents and so far, successfully digitized about 10,000 manuscripts (Joshi, 2016). Handwritten character identification applications employed

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“…The mentioned approaches, coupled with efforts to conduct competitions on various aspects of historical document layout analysis have aided progress in this area [27]- [29]. A variety of layout parsing approaches, including those employing the modern paradigm of deep learning, have been proposed for Indic [18], [20], [21], [30] and South-East Asian [14], [24], [31]- [33] palm-leaf and paper manuscript images. However, existing approaches typically employ brittle hand-crafted features or demonstrate performance on datasets which are limited in terms of layout diversity.…”

Section: Related Workmentioning

confidence: 99%

Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts

Prusty

Aitha

Trivedi

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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Historical palm-leaf manuscript and early paper documents from Indian subcontinent form an important part of the world's literary and cultural heritage. Despite their importance, large-scale annotated Indic manuscript image datasets do not exist. To address this deficiency, we introduce Indiscapes, the first ever dataset with multi-regional layout annotations for historical Indic manuscripts. To address the challenge of large diversity in scripts and presence of dense, irregular layout elements (e.g. text lines, pictures, multiple documents per image), we adapt a Fully Convolutional Deep Neural Network architecture for fully automatic, instance-level spatial layout parsing of manuscript images. We demonstrate the effectiveness of proposed architecture on images from the Indiscapes dataset. For annotation flexibility and keeping the non-technical nature of domain experts in mind, we also contribute a custom, webbased GUI annotation tool and a dashboard-style analytics portal. Overall, our contributions set the stage for enabling downstream applications such as OCR and word-spotting in historical Indic manuscripts at scale.

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Classification of Handwritten Tamil Characters in Palm Leaf Manuscripts Using SVM Based Smart Zoning Strategies

Cited by 8 publications

References 3 publications

A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts

A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts

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

Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts

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