DIGITALIZATION OF TAMIL HANDWRITTEN CHARACTERS RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS(CNN)
Ram Kumar S, Sai Vignesh M, Amalarani Sivamurugan, Karthikeyan Shanmugam · International journal of advance research and innovative ideas in education · 2020
Now-a-days digitalization becomes an important one for documents preservation. Some Tamil Handwritten characters need preservation, like land documents etc. So we try to overcome the difficulty of paper preservation by digitalizing it. The aim of this Project is to require Handwritten set of Tamil Characters as input within the format of image to process the character, train the Convolution Neural Network algorithm to acknowledge the pattern and convert the recognized characters to a Printed document. Convolutional Neural Network then attempts to work out if the computer file matches a pattern that the Neural Network has memorized. Optical Character Recognition deals with a crucial concern issue of handwritten character classification. To beat the difficulty of knowledge recognition among similarities, Convolutional Neural Network will provide more accuracy of character recognition. Convolutional Neural Networks (CNN) are playing an important role nowadays in every aspect of computer vision applications. The art of CNN is used in recognizing Tamil handwriten characters in offline mode. CNNs differ from traditional approach of Tamil Handwritten Character Recognition (THCR) in extracting the features by the methods of preprocessing ,normalization, feature extraction and classification .we've developed a CNN model from scratch by training the model with the Tamil characters in offline mode and We have achieved good accuracy results on obtained datasets. This work is for digitalizing offline THCR using deep learning technique.