Recognition of Tamil handwritten character using modified neural network with aid of elephant herding optimization

Nowadays, recognition of machine printed or hand printed document is essential part in applications. Optical character recognition is one of the techniques which are used to convert the printed or hand written file into its corresponding text format. Tamil is the south Indian language spoken widely in Tamil Nadu. It has the longest unbroken literary tradition amongst Dravidian language. Tamil character recognition (TCR) is one of the challenging tasks in optimal character recognition. It is used for recognizing the characters from scanned input digital image and converting them into machine editable form. Recognition of handwritten in Tamil character is very difficult, due to variations in size, style and orientation angle. Character editing and reprinting of text document that were printed on paper are time consuming and low accuracy. In order to overcome this problem, the proposed technique utilizes effective Tamil character recognition. The proposed method has four main process such as preprocessing process, segmentation process, feature extraction process and recognition process. For preprocessing, the input image is fed to Gaussian filter, Binarization process and skew detection technique. Then the segmentation process is carried out, here line and character segmentation is done. From the segmented output, the features are extracted. After that the feature extraction, the Tamil character is recognized by means of optimal artificial neural network. Here the traditional neural network is modified by means of optimization algorithm. In neural network, the weights are optimized by means of Elephant Herding Optimization. The performance of the proposed method is assessed with the help of the metrics namely Sensitivity, Specificity and Accuracy. The proposed approach is experimented and its results are analyzed to visualize the performance. The proposed approach will be implemented in MATLAB.