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HANDWRITING RECOGNITION FOR EXAM PAPER DIGITIZATION

  • Unique Paper ID: 168906
  • Volume: 11
  • Issue: 5
  • PageNo: 2310-2315
  • Abstract:
  • This research work represents a significant advancement in handwriting recognition by harnessing the power of CNNs to accurately classify handwritten characters. The systematic preprocessing of handwritten images and the strategic design of the CNN architecture enable the model to effectively capture intricate spatial features. Moreover, the integration of data augmentation and dropout regularization techniques enhances the robustness and generalization capability of the model, resulting in reliable performance on diverse datasets. With its ability to handle various types of handwritten content, including digits, symbols, and words, the proposed system offers a versatile solution for a wide range of applications in document processing and analysis. Furthermore, its seamless integration into existing workflows, such as exam paper grading, underscores its practical utility and potential to revolutionize traditional manual processes.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 5
  • PageNo: 2310-2315

HANDWRITING RECOGNITION FOR EXAM PAPER DIGITIZATION

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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