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Optical character recognition system for Baybayin scripts using support vector machine.

Rodney Pino1, Renier Mendoza1, Rachelle Sambayan1

  • 1Institute of Mathematics, University of the Philippines Diliman, Quezon City, Metro Manila, Philippines.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary

This study developed a Support Vector Machine (SVM) system to distinguish Baybayin and Latin characters, achieving high accuracy for script recognition and classification. The system effectively handles mixed-script documents, crucial for the Philippines

Keywords:
BaybayinBaybayin script identificationLatin script identificationOptical character recognitionSupport vector machine

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Area of Science:

  • Computational Linguistics
  • Pattern Recognition
  • Natural Language Processing

Background:

  • The Philippines officially recognized Baybayin as its national writing system, necessitating systems for handling documents with both Baybayin and Latin scripts.
  • Existing research lacks automated methods for discriminating and classifying characters from these two distinct writing systems.

Purpose of the Study:

  • To propose and evaluate a novel system for discriminating and classifying characters from both Baybayin and Latin scripts.
  • To address the challenges posed by mixed-script documents in the Philippines.

Main Methods:

  • Development of a character normalization technique to identify script origin (Baybayin or Latin).
  • Implementation of Support Vector Machine (SVM) for four classification tasks: script recognition, Baybayin character classification, Latin character classification, and Baybayin diacritics classification.
  • Creation of a new dataset comprising Baybayin, its diacritics, and Latin characters.

Main Results:

  • High performance across all classification tasks, with script recognition achieving 98.5% accuracy and Baybayin character classification reaching 96.51% accuracy.
  • Baybayin diacritics classification achieved perfect scores (100% accuracy, precision, recall, and F1 Score).
  • Latin character classification demonstrated strong results with 95.8% accuracy.

Conclusions:

  • The proposed SVM-based system effectively discriminates and classifies Baybayin and Latin characters, providing a robust solution for mixed-script document processing.
  • This study represents the first use of SVM for Baybayin script recognition and introduces a valuable new dataset for future research.