Paper Title
OFFLINE HANDWRITTEN CHARACTER RECOGNITION SYSTEM – A REVIEW
Article Identifiers
Registration ID: IJNRD_216695
Published ID: IJNRD2403638
: http://doi.one/10.1729/Journal.38714
Keywords
Handwritten Character Recognition, Neural Network, OpenCV, Android, Jupyter, Tensorflowlite, Keras, Python, OCR, Image Processing, Feature Extraction
Abstract
In this paper we present an innovative method for offline handwritten character detection using deep neural networks. In today world it has become easier to train deep neural networks because of availability of huge amount of data and various Algorithmic innovations which are taking place. Now-a-days the amount of computational power needed to train a neural network has increased due to the availability of GPU's and other loud based services like Google Cloud platform and Amazon Web Services which provide resources to train a Neural network on the cloud. We have designed a image segmentation based Handwritten character recognition system. In our system we have made use of OpenCV for performing Image processing and have used Jupyter, Tensorflowlite and Keras for training a the neural Network with the. We have developed this system using python programming language.
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How To Cite (APA)
Dr. Sangita Patil , Pranjali Kulkarni, Pratik Bevnale, Roopam Wadkar, & Anjali Chavan (March-2024). OFFLINE HANDWRITTEN CHARACTER RECOGNITION SYSTEM – A REVIEW. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), g327-g331. http://doi.one/10.1729/Journal.38714
Issue
Volume 9 Issue 3, March-2024
Pages : g327-g331
Other Publication Details
Paper Reg. ID: IJNRD_216695
Published Paper Id: IJNRD2403638
Downloads: 000122010
Research Area: Engineering
Author Type: Indian Author
Country: Pune, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2403638.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403638
Crossref DOI: http://doi.one/10.1729/Journal.38714
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