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Kurdish Handwritten character recognition using deep learning techniques.

Rebin M Ahmed1, Tarik A Rashid2, Polla Fattah3

  • 1IT Department, Faculty of Aplied Science, Tishk International University, Erbil, Iraq.

Gene Expression Patterns : GEP
|October 4, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a deep learning model for Kurdish handwriting recognition, achieving 83% accuracy. This breakthrough enables digital processing of Kurdish documents, making them searchable and accessible.

Keywords:
Convolutional neural networkDatabaseKurdish alphabetOffline handwriting recognition

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Area of Science:

  • Pattern Recognition
  • Image Processing
  • Artificial Intelligence

Background:

  • Handwriting recognition systems are advanced for many languages but lack support for Kurdish.
  • Kurdish (Sorani) uses a modified Arabic/Persian script with 34 characters.
  • Existing systems for other languages demonstrate the potential of deep learning.

Purpose of the Study:

  • To design and develop a deep learning model for recognizing handwritten Kurdish characters.
  • To create a comprehensive database for Kurdish handwritten characters.
  • To evaluate the model's performance and accuracy for Kurdish handwriting recognition.

Main Methods:

  • A Deep Convolutional Neural Network (CNN) model was employed.
  • A custom database of over 40,000 handwritten Kurdish character images was created.
  • The CNN model was trained using the created database for classification and recognition.

Main Results:

  • The proposed system achieved an 83% accuracy rate in testing.
  • The model demonstrated a 96% accuracy rate during training.
  • The deep learning model shows promising performance comparable to systems for other languages.

Conclusions:

  • The developed deep learning model effectively recognizes handwritten Kurdish characters.
  • The creation of a dedicated Kurdish handwriting database is a significant contribution.
  • This work paves the way for digital accessibility and processing of Kurdish handwritten documents.