Open AccessRecognition of Handwritten Characters using Deep Convolutional Neural Network
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f1064.0486s419
Subject(s) - computer science , intelligent word recognition , convolutional neural network , artificial intelligence , character (mathematics) , speech recognition , intelligent character recognition , segmentation , character recognition , pattern recognition (psychology) , telugu , optical character recognition , kanji , artificial neural network , disk formatting , chinese characters , image (mathematics) , mathematics , geometry , operating system
Handwritten character recognition (HCR) mainly entails optical character recognition. However, HCR involves in formatting and segmentation of the input. HCR is still an active area of research due to the fact that numerous verification in writing style, shape, size to individuals. The main difficult part of Indian handwritten recognition has overlapping between characters. These overlapping shaped characters are difficult to recognize that may lead to low recognition rate. These factors also increase the complexity of handwritten character recognition. This paper proposes a new approach to identify handwritten characters for Telugu language using Deep Learning (DL). The proposed work can be enhance the recognition rate of individual characters. The proposed approach recognizes with overall accuracy is 94%.