This paper is published in Volume-6, Issue-3, 2020
Area
Computer Science And Engineering
Author
Anurag G., Deepak S. D., Harsha J. K., C. Sagar Patil, Hemalatha M.
Org/Univ
Don Bosco Institution of Technology, Bengaluru, Karnataka, India
Pub. Date
16 June, 2020
Paper ID
V6I3-1541
Publisher
Keywords
Convolutional Neural Network (CNN), TensorFlow, Python, OpenCV, Flask, Jinja Template, Kerass, Fetch API, CSS Frameworks, Representational State Transfer

Citationsacebook

IEEE
Anurag G., Deepak S. D., Harsha J. K., C. Sagar Patil, Hemalatha M.. Handwritten text pattern recognition, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Anurag G., Deepak S. D., Harsha J. K., C. Sagar Patil, Hemalatha M. (2020). Handwritten text pattern recognition. International Journal of Advance Research, Ideas and Innovations in Technology, 6(3) www.IJARIIT.com.

MLA
Anurag G., Deepak S. D., Harsha J. K., C. Sagar Patil, Hemalatha M.. "Handwritten text pattern recognition." International Journal of Advance Research, Ideas and Innovations in Technology 6.3 (2020). www.IJARIIT.com.

Abstract

Character recognition from handwritten images is of great interest in the pattern recognition research community for their good application in many areas. To implement the system, it requires two steps, viz., feature extraction followed by character recognition based on any classification algorithm. A convolutional neural network (CNN) is an excellent feature of extractor and classifier. It is having multiple applications fields such as robotics, medicine, and security and surveillance. In this article, CNN is implemented for the NIST dataset with appropriate parameters for training and testing the system. The system provides accuracy of up to 94%, which is better with respect to others. It also takes a very low amount of time for training the system.