
Handwritten Character and Digit Recognition Using Convolutional Neural Network
Author(s) -
Priyank Patel,
Roshan Shinde,
Siddhesh Raut,
Sheetal Mahadik
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-v4-i3-023
Subject(s) - computer science , convolutional neural network , artificial neural network , artificial intelligence , classifier (uml) , speech recognition , arabic numerals , mobile device , natural language processing , operating system
The necessity for quick and precise content section on little handheld PCs has prompted a resurgence of interest in on-line word recognition utilizing counterfeit neural Networks. Old style strategies are consolidated and improved to give strong recognition of hand-printed English content. The focal idea of a neural net as a character classifier gives a legitimate base to are cognition framework; long-standing issues comparative with preparing, speculation, division, probabilistic formalisms, and so forth, need to settled, notwithstanding, to instigate astounding execution. assortment of developments in a manner to utilize a neural net as a classifier in a very word recognizer are introduced: negative preparing, stroke twisting, adjusting, standardized yield blunder, mistake accentuation, numerous portrayals, quantized loads, and incorporated word division all add to effective and hearty execution.