HANDWRITTEN CHARACTER RECOGNITION USING DEEP LEARNING

Abstract

Handwritten character identification is a topic that has been researched for years and is an area of interest for the community of Pattern recognition researchers since It may be put to use in a wide range of fascinating applications. all across the field. This subject is a difficult challenge as a task because each person has their own unique writing style. SVM, ANN, and CNN models are some of the available options for handling this problem's many different ways and approaches. HCR is a need in the modern world since it assists us in a variety of fields of public domain, which makes it all the more vital to study in depth. Off-line digit recognition and online digit recognition are both examples of the hybrid character recognition (HCR) category. In this study, we review the many existing algorithms that have been implemented to get the better knowledge of the course, and we will come to a conclusion on the best strategies that are currently being developed for HCR. HCR for Devanagari is carried out by the performance of a computational device that accepts input from documents, screens, photos, and other responsive devices and believe to provides output by reading those images as an ASCII or UNICODE format. This theory is supported by the fact that computers have become increasingly powerful in recent years. Sanskrit, Nepali, Marathi, and Hindi are some of the languages that are represented in Devanagari. This script is a blend of numerous languages. This implementation is more important because the design of upper-case and lower-case characters in Devanagari are more complicated than in most other languages out there. Comparatively speaking, the set of characters and digits used in Devanagari is more complicated than the set of characters used in the English language. Character recognition has been hampered by the absence of verified datasets including Devanagari, which has made the task more difficult to do in the field.