10.35940/ijeat.D6662.049420
https://zenodo.org/records/5558522
oai:zenodo.org:5558522
M. Sornam
M. Sornam
Department of Computer Science, University of Madras, Chennai, India.
C. Vishnu Priya
C. Vishnu Priya
Department of Computer Science, University of Madras, Chennai, India.
Poornima Devi. M
Poornima Devi. M
Department of Computer Science, University of Madras, Chennai, India.
Intelligent Feature Extraction for Handwritten Tamil Mantra Recognition in Slogans by using PCA based Deep Convolutional Neural Network
Zenodo
2020
Convolutional Neural Network, Deep Learning, Mantras, PCA, Pattern Recognition, Slogans, Tamil Characters.
Blue Eyes Intelligence Engineering & Sciences Publication(BEIESP)
Blue Eyes Intelligence Engineering & Sciences Publication(BEIESP)
Publisher
2020-04-30
eng
2249-8958
Creative Commons Attribution 4.0 International
Offline handwritten identification of characters is a core problem in pattern matching. The main challenge for researchers in the identification of handwritten characters is inscribing individual styles. Tamil mantras identification is a challenging job due to many missing features in the mantras ' complex structure. The problem at hand is to break traditional hand-designed features. A new venture has been undertaken to automatically extract the complex features for recognition and classification from the complex structure bypassing the individual Tamil characters into the Convolutional Neural Network, a special type of deep learning network. The best convolutional model was chosen to improve efficiency by comparing different convolutional models that vary in activation functions, classifiers, and pooling functions. Principal Component Analysis (PCA) was used to select the top n eigenvectors from the image for better efficiency. So with the above trained best model with PCA for independent Tamil character images, handwritten Tamil fonts in the slogans (a group of characters) have been well recognized.