Open AccessHandwritten Character Recognition using Neural Network and Tensor Flow
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.f1294.0486s419
Subject(s) - character (mathematics) , computer science , convolutional neural network , artificial intelligence , pattern recognition (psychology) , character recognition , artificial neural network , field (mathematics) , intelligent word recognition , neocognitron , software , speech recognition , intelligent character recognition , time delay neural network , mathematics , image (mathematics) , geometry , pure mathematics , programming language
The paper will describe the best approach to get more than 90% accuracy in the field of Handwritten Character Recognition (HCR). There have been plenty of research done in the field of HCR but still it is an open problem as we are still lacking in getting the best accuracy. In this paper, the offline handwritten character recognition will be done using Convolutional neural network and Tensorflow. A method called Soft Max Regression is used for assigning the probabilities to handwritten characters being one of the several characters as it gives the values between 0 and 1 summing up to 1. The purpose is to develop the software with a very high accuracy rate and with minimal time and space complexity and also optimal.