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Updated: Sep 26, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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Visualization of Customized Convolutional Neural Network for Natural Language Recognition.

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Summary

This study introduces a novel Convolutional Neural Network (CNN) for recognizing handwritten Gurumukhi month names. The holistic approach achieved high accuracy, overcoming challenges in cursive script segmentation.

Keywords:
Gurumukhi scriptconvolutional neural networkperformance analysisword recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Segmenting cursive handwriting into characters is a significant challenge for analytical word recognition.
  • A holistic approach, processing entire words, offers a promising alternative for complex scripts like Gurumukhi.
  • Offline handwritten word recognition for Gurumukhi script requires specialized techniques.

Purpose of the Study:

  • To propose a Convolutional Neural Network (CNN)-based architecture for the offline handwritten word recognition of Gurumukhi month names.
  • To develop and evaluate a robust system for recognizing a complex Indic script.

Main Methods:

  • A holistic approach using a CNN architecture with five convolutional and three pooling layers was designed.
  • A custom dataset of 24,000 handwritten Gurumukhi month name images (50x50 pixels) was created.
  • Data was collected from 500 diverse writers across different age groups and professions.

Main Results:

  • The proposed CNN model achieved a training accuracy of approximately 97.03%.
  • The model demonstrated a validation accuracy of about 99.50% on the custom dataset.
  • The holistic approach proved effective for Gurumukhi handwritten word recognition.

Conclusions:

  • The developed CNN architecture is highly effective for offline handwritten Gurumukhi month name recognition.
  • The holistic approach successfully addresses the complexities of the Gurumukhi script.
  • The findings contribute to advancements in Indic script recognition and handwriting analysis.