Handwritten Recognition Using Convolutional Neural Networks

Handwritten Character recognition is the ability of a computer to receive and interpret intelligible handwritten input. Handwritten recognition is an emerging technology in image processing. Handwritten recognition is the wonders of empowering a machine to automatically realize the characters written in a user dialect. Character extraction has turned out to be a standout amongst the best candidates of innovation in the field of pattern recognition and artificial intelligence. In proposed technique, we present a handwritten character and digit recognition system based on different Deep learning technique. Handwritten character recognition plays an important role and is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In the proposed work, we propose a technique to recognize handwritten characters and digits using deep learning approaches like Convolutional Neural Network (CNN) With, Adaptive Moment (Adam) Estimation and Dense Neural Networks. The proposed system has been trained on samples of large set of database images and tested on samples images from user defines data set and from this experiment we achieved very high recognition results