Implementation of Handwritten Character Recognition using ANN and HCNN

Authors(2) :-Vijaylaxmi, Vinita Patil

The paper concentrates on a concealed control neural system (HCNN) based A"/HMM half breed approach which handles all the while both the worldwide pattem class variety and the neighborhood flag primitive variety. Gee is utilized, at the pattern class level to arrange diverse primitives in different requests. One HCNN is connected to demonstrate flag primitives in each HMM state as the outflow likelihood estimator. The control flag of HCNN adapts to the primitive variety retention assignment. The proposed technique was connected to the on-line cursive penmanship acknowledgment issue and contrasted and our past comparative frameworks on the UNIPEN penmanship database.

Authors and Affiliations

Department of Digital Communication and Networking, Godutai Engineering College for Women, Kalaburagi, Karnataka, India 2Department of Electronics and Communication Engineering, Godutai Engineering College for Women, Kalaburagi, Karnataka, India
Vinita Patil

Cooperative, Neural, HCCNN.

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 914-917
Manuscript Number : CSEIT1725199
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Vijaylaxmi, Vinita Patil, "Implementation of Handwritten Character Recognition using ANN and HCNN", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.914-917, September-October-2017.
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