Real Time Character Recognition using Convolution Neural Network

Abstract

Abstract: Handwritten recognition of character (HCR) is a significant element in the current world and one of the focused fields in image processing and pattern recognition research. Handwritten recognition of character refers to the process of converting hand-written character into printed/word file character that in many applications may greatly enhance the interaction of man and machine. The styles, varied sizes and orientation angles of the current characters are tough to parse with large variances. In addition, it is hard to split cursive handwritten text as the edges cannot be clearly seen. Many ways of recognizing handwritten data are available. The proposed research is based on 5*5 convolution neural network where the performance of the system has been enhanced in terms of accuracy, precision and recall the data set. The research utilized the real time photos. The processing approaches are followed by binarization, skeletonisation, dilution, resizing, segmentation and extraction. The character characteristics are sent to CNN to train the models after preprocessing. The research achieved 92% accuracy and time delay while detecting the real time Images.