Open AccessHandwritten Digit Recognition Using Deep Learning
Author(s) -
P M Bhagyashree,
Lekkala Likhitha,
D. S. Rajesh
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/cseit217439
Subject(s) - computer science , digit recognition , handwriting , convolutional neural network , artificial intelligence , intelligent character recognition , task (project management) , pattern recognition (psychology) , speech recognition , handwriting recognition , optical character recognition , character recognition , numerical digit , intelligent word recognition , deep learning , character (mathematics) , artificial neural network , feature extraction , image (mathematics) , arithmetic , mathematics , geometry , management , economics
Traditional systems of handwritten Digit Recognition have depended on handcrafted functions and a massive amount of previous knowledge. Training an Optical character recognition (OCR) system primarily based totally on those stipulations is a hard task. Research in the handwriting recognition subject is centered on deep learning strategies and has accomplished breakthrough overall performance in the previous couple of years. Convolutional neural networks (CNNs) are very powerful in perceiving the structure of handwritten digits in ways that assist in automated extraction of features and make CNN the most appropriate technique for solving handwriting recognition problems. Here, our goal is to attain similar accuracy through the use of a pure CNN structure.CNN structure is proposed to be able to attain accuracy even higher than that of ensemble architectures, alongside decreased operational complexity and price. The proposed method gives 99.87 accuracy for real-world handwritten digit prediction with less than 0.1 % loss on training with 60000 digits while 10000 under validation.