Intelligent Feature Extraction for Handwritten Tamil Mantra Recognition in Slogans by using PCA based Deep Convolutional Neural Network

Abstract

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.