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A Hybrid Rule-Based and Machine Learning System for Arabic Check Courtesy Amount Recognition.

Irfan Ahmad1,2

  • 1Information and Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel hybrid system for recognizing courtesy amounts on Arabic bank checks using Indian digits. The system achieves 67.4% accuracy at the amount level, establishing a new benchmark for this task.

Area of Science:

  • Pattern Recognition
  • Machine Learning
  • Computer Vision

Background:

  • Courtesy amount recognition from bank checks is crucial.
  • Existing research lacks focus on Arabic checks with Indian digits.
  • Arabic check recognition presents unique challenges.

Purpose of the Study:

  • To develop an end-to-end system for Arabic check courtesy amount recognition.
  • To investigate and compare segmentation-based and segmentation-free approaches.
  • To establish a baseline for future research in this domain.

Main Methods:

  • Developed a hybrid system combining rule-based and machine learning modules.
  • Implemented both segmentation-based and segmentation-free recognition strategies.
  • Evaluated the system on the CENPARMI dataset of Arabic bank checks.
Keywords:
Arabic check image processingcourtesy amount recognitiondigit recognitionmachine learningpattern recognition

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Main Results:

  • Achieved 67.4% accuracy at the amount level.
  • Achieved 87.15% accuracy at the digit level.
  • Demonstrated the system's effectiveness on a dataset of 626 check images.

Conclusions:

  • The developed hybrid system provides a robust solution for Arabic check courtesy amount recognition.
  • The system's performance sets a new benchmark for the field.
  • Identified areas for future research to further improve accuracy and applicability.