Abstract
Tamil is one of the ancient Indian languages which has a vast collection of literature in the form of palm leaf, stones, metal plates, and other materials. Palm leaf manuscript was a broad tool to narrate medicines, literature, drama, and many more. Recognition of the characters written in palm leaf manuscripts is still an open task because of the need for digitization and transcription. In this paper, the recurrent neural network (RNN) is used to train the features of the characters extracted from the palm leaf. This method contains a preprocessing method to eliminate noise; then the character is segmented from the image and trained using the bidirectional long short-term memory (BLSTM) network. A feature vector with nine zones of character strokes is used to train and test the characters. A rich set of characters are used to train the features of the characters. This method provides better recognition accuracy than the other neural network-based character recognition.
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Hereby the authors acknowledge Agama Academy for providing a rich set of digital archive of Tamil palm leaf manuscript for this research work.
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Athisayamani, S., Robert Singh, A., Sivanesh Kumar, A. (2021). Recurrent Neural Network-Based Character Recognition System for Tamil Palm Leaf Manuscript Using Stroke Zoning. In: Ranganathan, G., Chen, J., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 145. Springer, Singapore. https://doi.org/10.1007/978-981-15-7345-3_14
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DOI: https://doi.org/10.1007/978-981-15-7345-3_14
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