Recurrent Neural Network-Based Character Recognition System for Tamil Palm Leaf Manuscript Using Stroke Zoning

Athisayamani, Suganya; Singh, Anshuman; Kumar, Ashish

doi:10.1007/978-981-15-7345-3_14

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…Tamil is among the world's oldest and most recognized languages, having a rich literary heritage. In the ancient period, poets employed Palm leaves, notably in Tamil Nadu, to conceal information [6]. Sangam literature, masterpieces, Vaishnava, Saiva, medicinal works, gastronomy, astrology, Vaastu, gems, music, dance and theatre, and Siddha are all included in the ancient literature.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…When we speak of precision, what we are truly referring to is the correctness of our categorisation. The proportion of accurate predictions made in comparison to the total number of samples fed into the system is one way to evaluate the performance of a classification algorithm [8]. It is easy to be fooled by the high level of accuracy that is stated for classifications.…”

Section: Introductionmentioning

confidence: 99%

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2022

IJIVP

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Biometric techniques are currently among the most widely used methods all over the world for determining a person identity. This trend is expected to continue in the near future. In this study, we focused on palm vein is used as a strategy to improve biometric authentication systems by combining a method that is based on texture with a method that is based on a convolutional neural network (CNN). The simulation is used to test the performance of the model on several different datasets. In simulations, the suggested method routinely achieves better results than the current best practice on each and every dataset.

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Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review

Jyothi¹,

Singh

2022

Artif Intell Rev

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Recurrent Neural Network-Based Character Recognition System for Tamil Palm Leaf Manuscript Using Stroke Zoning

Cited by 10 publications

References 9 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

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Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review

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