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Implementation of Handwritten Character Recognition using ANN and HCNN

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  • Vijaylaxmi
  • Vinita Patil

Abstract

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.

Suggested Citation

  • Vijaylaxmi & Vinita Patil, 2017. "Implementation of Handwritten Character Recognition using ANN and HCNN," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(5), pages 914-917, October.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i5:id:hcseit1725199
    Note: Article URL: https://ijsrcseit.com/CSEIT1725199
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