State Machine Induction with Positive and Negative Training for Thai Character Recognition

Kruatrachue, Boontee; Pantrakarn, Nattachat; Siriboon, Kritawan

doi:10.1109/icccas.2007.4348209

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…It classifies patterns on the basis of predictable properties of neural networks. However, it represents only a little amount of information about semantic from a network [14]- [16].…”

Section: Neural Network Based Approachmentioning

confidence: 99%

Text Analysis and Information Retrieval of Historical Tamil Ancient Documents Using Machine Translation in Image Zoning

Vellingiriraj¹,

Balamurugan²,

Balasubramanie³

2016

IJLLL

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“…It classifies patterns on the basis of predictable properties of neural networks. However, it represents only a little amount of information about semantic from a network [14]- [16].…”

Section: Neural Network Based Approachmentioning

confidence: 99%

Text Analysis and Information Retrieval of Historical Tamil Ancient Documents Using Machine Translation in Image Zoning

Vellingiriraj¹,

Balamurugan²,

Balasubramanie³

2016

IJLLL

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“…Statistical Analysis Approach. Statistical Pattern Recognition uses statistical and/or probabilities functions for building a recognition algorithm [3,4,5,6,7,8,9]. The input features are extracted from a set of characteristic pattern measurements.…”

Section: Background Materialsmentioning

confidence: 99%

Lanna Handwritten Character Recognition on Historical Documents Using Feature Extraction

Khankasikam

2012

AMM

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The objective of this paper is to develop methodology that can recognize the Lanna handwritten character on historical documents by using character feature extraction technique. Historical documents are national treasures. Insignificant effort has been made to preserve Lanna historical documents. Other nations such as Egypt, China and Greece are investing a large effort in restoring and preserving their national historical documents. As a starting point, the focus is on using one Lanna historical document for performing experiments and verifying recognition methods available in this research area. The proposed system consists of three modules, which are image preprocessing module, feature extraction module and character recognition module. The details of each module are following: first, the input image is transformed into a suitable image for feature extraction module. Second, the proposed system extracts character features from the image. Finally, the extracted character information, which is kept in form of bit string, is calculated a similarity value for recognition result. The experiment was conducted on more than 4,000 Lanna handwritten characters by using 10-fold cross-validation classification method which is using 3,600 for training characters and 400 for testing character. The cross-validation process is repeated 10 times, with each of the 10 subsets used exactly once as the validation data. The precision of the proposed system is around 89.73 percent.

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“…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 three essential methods for digitizing historical records: the statistical (Nagy, et al 1992; Jose and Wahi 2013; Vellingiriraj et al 2016), structural or syntactic (Chen and Lopresti 2012; Pornpanomchai and Daveloh 2007), and neural network‐based approaches (Kruatrachue, Pantrakarn, & Siriboon, et al 2007; Sanguansat, et al 2004; Budsayaplakorn, Asdornwised, & Jitanpunkul, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…three essential methods for digitizing historical records: the statistical (Nagy, et al 1992;Jose and Wahi 2013;Vellingiriraj et al 2016), structural or syntactic (Chen and Lopresti 2012;Pornpanomchai and Daveloh 2007), and neural network-based approaches (Kruatrachue, Pantrakarn, & Siriboon, et al 2007;Sanguansat, et al 2004;Budsayaplakorn, Asdornwised, & Jitanpunkul, 2003). Kumar, Sreekumar and Athvankar (2009) emphasized that the palm leaf practice is one of the oldest methods of language and classical encryption in India, especially in Southern India.…”

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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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State Machine Induction with Positive and Negative Training for Thai Character Recognition

Cited by 4 publications

References 4 publications

Text Analysis and Information Retrieval of Historical Tamil Ancient Documents Using Machine Translation in Image Zoning

Text Analysis and Information Retrieval of Historical Tamil Ancient Documents Using Machine Translation in Image Zoning

Lanna Handwritten Character Recognition on Historical Documents Using Feature Extraction

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

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