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[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script

Author

Listed:
  • Sandhya Sharma
  • Sheifali Gupta
  • Deepali Gupta
  • Sapna Juneja
  • Hamza Turabieh
  • Lokesh Sharma
  • Zelalem Kiros Bitsue

Abstract

Automation of Postal systems has the major research scope in the field of automation. To create Postal Automation set‐up for countries like India is a tedious task if compared with other countries because of India’s multiscript and multilingual behavior. This work will help in recognizing the “Gurmukhi” handwritten district names of the State Punjab. To recognize the district names, a CNN‐based architecture is proposed by employing a Holistic approach. For this, an image database of 22000 samples is prepared having 1000 sample images for every district name which is collected from 500 different writers. Maximum accuracy on validation data achieved by the proposed Model is 99%.

Suggested Citation

  • Sandhya Sharma & Sheifali Gupta & Deepali Gupta & Sapna Juneja & Hamza Turabieh & Lokesh Sharma & Zelalem Kiros Bitsue, 2022. "[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:6580839
    DOI: 10.1155/2022/6580839
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    1. Annu Dhankhar & Sapna Juneja & Abhinav Juneja & Vikram Bali, 2021. "Kernel Parameter Tuning to Tweak the Performance of Classifiers for Identification of Heart Diseases," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global Scientific Publishing, vol. 12(4), pages 1-16, July.
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