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Algorithm based on normal coordinate vectors with 16 segments for the data fusion from hand-written Arabic text

Said S Saloum¹, Iván García-Magariño^2,3

¹College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia.

Peerj. Computer Science

|October 4, 2021

Summary

This study introduces a new method for handwriting recognition using coordinate vectors to overcome challenges like letter variations and deformations. The approach achieved 92.8% accuracy, improving data interpretation in fields like healthcare.

Keywords:

Data fusion Handwriting recognition MATLAB Normal coordinate vectors

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Area of Science:

Computer Science
Artificial Intelligence
Pattern Recognition

Background:

Hand-written text recognition is crucial for fields like healthcare and law enforcement where paper notes are common.
Challenges include diverse writing styles and character deformations, hindering accurate data interpretation and fusion.

Purpose of the Study:

To develop a novel handwriting recognition approach using coordinate vectors to address deformation and variation issues.
To enhance the accuracy of automatic handwriting interpretation for diverse applications.

Main Methods:

A novel approach applying coordinate vectors to identify similarities in deformed handwriting.
Implementation using 16 segments for detailed character analysis.
Utilized a machine learning approach with MATLAB, evaluating 22 technique combinations.

Main Results:

Achieved a high accuracy rate of 92.8% for handwriting recognition.
Demonstrated effectiveness with ensemble and bagged tree machine learning models.
The coordinate vector method successfully identified similarities across various deformations.

Conclusions:

The proposed coordinate vector-based handwriting recognition method shows significant promise.
This technique offers a robust solution for interpreting handwritten records, particularly in professional settings.
The high accuracy suggests potential for widespread adoption in data-driven fields.